Estimating the 3D alignment of migratory insects is essential for understanding their 3D directional behavior. The existing approach reconstructs 3D alignment using azimuthal angle measurements from dual-view entomological radars. However, the measurement errors often propagate into the reconstructed 3D alignment, causing significant inaccuracies, especially under low signal-to-noise ratio conditions. To overcome this problem, this letter proposes a new method based on a multi-view, multi-frequency, and full-polarization entomological radar system. The method directly estimates insect 3D alignment by integrating polarization scattering matrices (SMs) from multiple views and frequencies, leveraging the assumption of scattering symmetry in insect bodies. For symmetric targets, the off-diagonal SM elements vanish when the polarization direction aligns with the symmetry plane. Based on this theory, an optimization problem is formulated to estimate 3D alignment by minimizing the sum of the powers of off-diagonal elements across multi-view and multi-frequency SMs. Both simulations and field experiments demonstrate that the proposed method achieves substantially higher accuracy than the traditional method.

The combination of Integrated Sensing and Communication (ISAC) and Mobile Edge Computing (MEC) enables devices to simultaneously sense the environment and offload data to the base stations (BS) for intelligent processing, thereby reducing local computational burdens. However, transmitting raw sensing data from ISAC devices to the BS often incurs substantial fronthaul overhead and latency. This paper investigates a three-tier collaborative inference framework enabled by Integrated Sensing, Communication, and Computing (ISCC), where cloud servers, MEC servers, and ISAC devices cooperatively execute different segments of a pre-trained deep neural network (DNN) for intelligent sensing. By offloading intermediate DNN features, the proposed framework can significantly reduce fronthaul transmission load. Furthermore, multiple-input multiple-output (MIMO) technology is employed to enhance both sensing quality and offloading efficiency. To minimize the overall sensing task inference latency across all ISAC devices, we jointly optimize the DNN partitioning strategy, ISAC beamforming, and computational resource allocation at the MEC servers and ISAC devices, subject to sensing beampattern constraints. We also propose an efficient two-layer optimization algorithm. In the inner layer, we derive closed-form solutions for computational resource allocation using the Karush-Kuhn-Tucker conditions. Moreover, we design the ISAC beamforming vectors via an iterative method based on the majorization–minimization and weighted minimum mean square error techniques. In the outer layer, we develop a cross-entropy-based probabilistic learning algorithm to determine an optimal DNN partitioning strategy. Simulation results demonstrate that the proposed framework substantially outperforms existing two-tier schemes in inference latency.

Synthetic Aperture Radar (SAR) tomography is an essential method for acquiring data used in the reconstruction of urban three-dimensional (3D) models. With advancements in SAR technology, polarization has been integrated into tomography for accurately identifying and locating target structures. Recent studies focus on independently enhancing the ability of compressed sensing (CS) or spectral estimation methods to utilize polarimetric data. Since urban areas contain both small-scale man-made objects and large-scale homogeneous or distributed targets, using either of these two methods alone cannot balance accuracy with the preservation of 3D details. To address this, this study presents an adaptive polarimetric SAR (PolSAR) tomography method based on scattering mechanism classification, which leverages the strengths of both CS and spectral estimation techniques. In this study, we focus on the inversion of a single scatterer, without any attempt to separate multiple scatterers. The proposed method classifies pixels into four categories based on their scattering mechanisms, typically corresponding to manmade and distributed targets. The processing algorithms for CS and spectral estimation are then selected automatically based on the classification. For the pixels processed using spectral estimation, an adaptive window is also employed to compute the covariance matrix. Furthermore, the method employs an optimal polarization basis projection technique to effectively utilize polarization information during the tomography process. Experimental results show that this approach significantly improves both reconstruction accuracy and structural preservation.

Radial velocity data of Doppler weather radars are crucial for wind field retrieval and nowcasting, but multi-fold velocity aliasing in short-wavelength radar systems (e.g., X/Ku-band) severely restricts their application. Conventional velocity dealiasing methods relying on velocity continuity fail in such scenarios due to unreliable initial reference velocity determination. To resolve this challenge, the Morphology-based Zero Iso-Doppler Contour (Zero-Isodop) Extraction and Velocity Correction Algorithm (MZI-VCA) is proposed for multi-fold velocity-aliased weather radar. The method first constructs a large-scale uniform horizontal wind model to characterize aliased zero-isodop distributions, then applies morphological operations combined with Depth-First Search (DFS) for candidate isodop extraction in Plan Position Indicator (PPI) images, and finally identifies the zero-isodop through spatial structure metrics. Leveraging the extracted zero-isodop, dealiasing is performed under spatial continuity constraints. Validations using both simulated multi-fold aliasing data (derived from S-band radar) and Ku-band weather radar observations demonstrate that MZI-VCA achieves superior performance compared to the traditional dealiasing method UNRAVEL. MZI-VCA achieves 100% dealiasing success rate in correcting double- and triple-fold aliasing, with a success rate of 90.74% across all diverse multi-fold scenarios tested in 108 simulated volume scans. In contrast, UNRAVEL fails to resolve multi-fold aliasing, exhibiting only a 6.92% Probability of Detection (POD). The systematic method establishes a new framework for resolving multi-fold velocity aliasing in short-wavelength radar systems.

Animal migration poses risks to human health and economic stability, highlighting the need for effective monitoring. Weather radars are essential tools for monitoring migratory insects and birds. While S-band radars can accurately distinguish insect and bird echoes, X-band radar, offering higher resolution, has not been sufficiently explored, limiting its use in aerial ecological monitoring. In this paper, joint observational experiments were conducted to evaluate insect and bird echoes from S-band and X-band weather radars. The results show significant overlap in the polarization features on X-band radar, making existing algorithms unsuitable for X-band data. To address this issue, a point-surface feature fusion method is proposed. This approach extracts polarization variables to construct point-scale features for initial classification with statistical models. A residual network captures surface-scale morphological features, which are integrated with the point-scale recognition results. Finally, a feature fusion module generates the final classification. The method achieves a mean intersection-over-union (mIoU) of 84.56% and demonstrates high accuracy and robustness in historical data tests. This study enhances X-band radar’s ability to differentiate between insect and bird echoes, providing a new solution for aerial ecological monitoring.

For vertical-looking radars (VLRs), it is typically assumed that insects maintain a steady and approximately horizontal flight attitude as they pass through the radar beam, allowing for the estimation of insect morphological parameters by measuring the Radar Cross Section (RCS) for a ventral aspect. However, for tracking radars, which dynamically track and monitor insects, the attitude of the insect relative to the radar beam constantly changes. This dynamic change in attitude renders traditional insect morphological parameter estimation methods based on the ventral-aspect RCS ineffective. This paper proposes a novel method for estimating the morphological parameters of insects in non-horizontal flight attitudes. By determining the azimuth and pitch angles of the insect’s body axis relative to the radar antenna reference coordinate system and reconstructing the scattering matrix (SM) of the insect from non-horizontal attitudes to a horizontal attitude, we achieve the estimation of morphological parameters for insects in non-horizontal attitudes. The effectiveness of the proposed method is validated using a fully-polarimetric multi-angle observation dataset of 33 insects from 6 species measured in a microwave anechoic chamber. The mean relative errors in estimating the mass and length of the insects across 20 different observation angles are 20.06% and 12.85%. Compared to estimates obtained without making the correction, the accuracy of mass and body length estimation is improved by 19.80% and 13.79%, respectively.

Spaceborne Synthetic Aperture Radar (SAR) Along-Track Interferometry (ATI) serves as a primary approach to measure the Total Surface Current Vector (TSCV) of the ocean. However, single spaceborne SAR systems are constrained by limited observation perspectives, leading to challenges in measuring two-dimensional (2D) TSCV. To address the 2D TSCV inversion issue, a bistatic SAR that utilizes Geosynchronous SAR (GEO SAR) as the radiation source and Low Earth Orbit SARs (LEO SARs) as passive receivers is proposed. This bistatic SAR configuration outperforms existing bistatic SAR ATI systems in terms of cost efficiency as well as inter-satellite synchronization simplicity. For the GEO-LEO SAR system, we develop new interferometric signal models and processing methods to enable one-dimensional (1D) and 2D TSCV estimation. Secondly, to enhance measurement performance, a system configuration optimization algorithm that considered both imaging and ATI performances is applied. Optimization results demonstrate that a larger GEO SAR elevation angle improves estimation accuracy. Finally, based on the currently operating L-band GEO SAR system LSAR4-01, optimization and full-link ATI simulations are conducted, verifying the effectiveness of the optimization algorithm and highlighting the potential design of LEO SAR orbits for high accuracy ATI observations. Simulations achieve an imaging resolution of <100 m and a 2D TSCV inversion bias of 0.1 m/s. The influence of errors on simulation accuracy is also discussed in depth.

Space-surface bistatic synthetic aperture radar (SS-BSAR) system consists of a spaceborne synthetic aperture radar (SAR) transmitter and ground-based receivers. This system has the advantage of multiple angles for observation, which can improve imaging capability and deformation measurement dimensions by differential interferometric SAR (D-InSAR). With the increasing number of spaceborne SARs and various corresponding working modes, processing SS-BSAR data with public ephemeris (generally inaccurate) and unknown signal parameters is significant for the full use of illuminators. This noncooperative status will result in interferometric phase errors in repeat- pass SS bistatic InSAR (SS-BInSAR), leading to deterioration of deformation retrieval accuracy. To address this, this article focuses on the method and processing of noncooperative repeat-pass SS-BInSAR. First, a repeat-pass SS-BSAR interferometric model was established. Based on this, the impacts of time synchronization errors and orbit errors on repeat-pass interferometric phase are modeled. Furthermore, an end-to-end compensation approach is proposed for accurate interferometric processing. This approach includes accurate estimation for signal parameters, interferometric phase error elimination, and digital elevation models (DEMs) fusion recovered from multiple observations. Finally, a repeat-pass SS-BInSAR experiment utilizing the Chinese Lutan-1 as the transmitter is carried out to verify our methods. The results show a centimeter-level accuracy of deformation measurement by a single InSAR pair, indicating a great potential of SS-BInSAR in deformation retrieval.

In recent years, the rainfall estimator that combines the specific attenuation A and the specific differential phase${K} _{\text {DP}}$for X-band radar has been concerned and developed. However, the constraints of empirical coefficients and insufficient resolution of A and${K} _{\text {DP}}$estimates, as well as the uncertainties caused by the unknown shapes of raindrops, pose challenges to the estimator in maintaining accuracy of rainfall estimates. The high correlation between the differential reflectivity${Z} _{\text {DR}}$and the raindrop shape helps to mitigate the uncertainties associated with the variations of drop size distribution (DSD) and the unknown shapes of raindrops. However, as a power measurement,${Z} _{\text {DR}}$is inevitably affected by radar miscalibration, partial beam blockage (PBB), and bias from wet radome, which hinders its application for rainfall estimation. The backscattering differential phase$\delta _{\text {hv}}$is also strongly dependent on raindrop shape and is not affected by the above negative factors, so it has the potential to be the substitute for${Z} _{\text {DR}}$. Unfortunately, reliable method for estimating$\delta _{\text {hv}}$in rain is currently lacking. This article reviews an adaptive and high-resolution (HR) method for estimating A and${K} _{\text {DP}}$called adaptive and high-resolution empirical coefficient conditioning (AHRCC), and based on the outputs of AHRCC, proposes a method for estimating$\delta _{\text {hv}}$accurately, which mainly reduces the cumulative bias caused by path integral. In addition, an algorithm for rainfall estimation based on A,${K} _{\text {DP}}$, and$\delta _{\text {hv}}$is proposed to reduce the overestimation of rainfall caused by DSD variations and raindrop shape uncertainties, and the potential of retrieving characteristic raindrop sizes by$\delta _{\text {hv}}$is also explored.

Entomological radar utilizes full-polarization data to estimate insect orientation, which is essential for understanding the orientation mechanisms of migrating insects and predicting their trajectories. Traditional orientation estimation methods rely on the empirical assumption that maximum echo intensity occurs when the polarization direction aligns with the insect’s body axis. Orientation is then extracted by identifying the polarization direction corresponding to the maximum echo intensity, based on the polarization pattern or the scattering matrix (SM) measured by single-frequency radars. However, the accuracy of the estimated orientation is affected by noise and polarization errors. To further improve orientation accuracy, based on a new generation of multifrequency and full-polarization entomological radar, this article proposes a novel method. The approach integrates multifrequency SMs of an insect under the assumption of insect body symmetry. First, a parametric SM model, characterized by four independent parameters, including insect orientation, was developed based on the polarization theory that when the symmetry axis of a symmetric target aligns with the horizontal or vertical polarization direction, the cross-polarization elements in the SM are zero. Using this principle, a cost function was constructed by summing the cross-polarization powers across multifrequency SMs. By minimizing the cost function, the analytical formula for insect orientation estimation was derived. Simulations using data from 159 insects measured in an anechoic chamber, along with field measurements, demonstrated that the proposed method provides superior accuracy and robustness against noise and polarization errors compared to traditional single-frequency approaches.

Insects are categorized into two classes, “parallel (PA)” and “perpendicular (PE),” based on the relationship between radar cross section (RCS) values when the polarization direction is PA and PE to the insect body axis. Distinguishing between these classes is essential for accurately measuring insect orientation and morphological parameters. The current classification method relies on the relative phase sign of two eigenvalues from the insect’s polarization scattering matrix (SM). However, this method is susceptible to phase unwrapping errors and noise in practical applications. To enhance classification accuracy, the multifrequency characteristics of the relative amplitudes of SM eigenvalues, the polarization pattern shape, and insect class were analyzed using multifrequency SM data from both electromagnetic simulations and microwave anechoic chamber measurements. The analysis revealed that insect class can be distinguished based on the relative amplitude and phase of SM eigenvalues at two subfrequencies in the X-band. Building on this, a classification model for PA and PE insects was developed using the random forest (RF) algorithm, with the relative eigenvalues at 9.5 and 11.5 GHz as key features. Simulations demonstrated that the proposed method outperforms the traditional approach, particularly at low signal-to-noise ratios (SNRs). The model was then applied to a multifrequency, fully-polarimetric entomological radar, achieving 99.4% accuracy in distinguishing PA and PE insect classes based on body-axis alignment in the field. Finally, the reliability of the method was further validated through observations of freely flying migratory insects.

The global climate change has led to a sharp decline in the number and species diversity of aerial migratory animals. Breakthroughs in the field of ecological monitoring using weather radar enable large-scale and long-term ecological monitoring. However, the primary challenges are the compression of spectral details in base data products, which leads to a loss of scatterer-information, and bias caused by frequency offset in dual-polarized products, which affects classification consistency between radar sites. To address these challenges, we explored the multi-dimensional range-Doppler (RD) characteristics of migration traits from Level-I IQ data and proposed an echo classification method for insect and bird of weather radar. We employ adaptive linear filtering for clutter preprocessing, followed by morphological image process to extract biological connected domains, leveraging spectral feature differences between insects and birds. Subsequently, a hierarchical classifier model is developed for classification, complemented by a minimal value inflection points detection method to identify insect-bird coexistence. Our approach is implemented to support the large-scale monitoring of aerial animal migration in the network of S-band weather radar stations. Experiments conducted with five operational weather radars have comprehensively validated the benefits of the proposed method in the accurate classification of insect and bird echoes. Future work will concentrate on precise species identification and biological quantification within resolution volumes.

Polarimetric Synthetic Aperture Radar (PolSAR) holds significant utility in classification through exploiting polarization information. However, many sensors are dual-pol, and their capabilities of classification are limited by the absence of polarization information. This paper focuses on dual-pol classification, proposing a two-step dual-band classification strategy for the Wishart classifier. The first step involves leveraging two bands to category the strong scattering pixels. And the second step entails optimizing the classification of weak scattering pixels using interferometry coherence. We use practical data from the C and L bands, employing Support Vector Machine (SVM) as a comparative experiment to validate the effectiveness of our strategy. The total accuracy and Kappa of the Wishart classifier finally increase by 3.92% and 0.05, respectively, indicating the effectiveness of our strategy. In addition, the second step also independently improves the performance of SVM, suggesting its versatility. This study can offer a novel perspective for dual-pol and multi-band classification.

Computerized Ionospheric Tomography (CIT) serves as a crucial method for ionospheric monitoring, playing a significant role in space environment surveillance and earthquake prediction. Existing CIT techniques are mostly based on the Global Navigation Satellite System (GNSS) system, constrained by the distribution of receivers. CIT based on geosynchronous SAR (GEO SAR) presents a solution by leveraging Persistent Scatterer (PS) points within the scene. However, current CIT techniques using GEO SAR typically utilize PS points from a single SAR image. This paper introduces a novel approach – a GEO SAR-based long-term joint multi-image CIT method. This method enhances the exploitation of satellite data, offers a broader range of observation angles, and improves tomography accuracy. Finally, through a CIT experiment involving three GEO SAR images, the electron density distribution is derived with a time resolution of 10 minutes. The results align with the International Reference Ionosphere (IRI) data, validating the feasibility and advantages of the proposed method. It is noteworthy that the proposed method caters to the tomography observation mode of a single satellite and is adaptable to each satellite within a satellite formation.

Spectrum width (SW) is crucial for warning of severe weather, which is commonly estimated from a ratio of lag-0 to lag-1 autocorrelations (R0/R1). For traditional weather radars, the estimators with higher lags autocorrelation functions (ACFs) have been developed to enhance the performance at low signal-to-noise ratio (SNR) and narrow SW. In the alternate transmission mode, the interval between co-polarimetric signals is twice the pulse repetition time (PRT). Thus, the estimators for traditional radars cannot be applied to the ones with alternate transmission of horizontal and vertical polarized wave (AHV) modes. This article introduces the cross correlation functions (CCFs) to derive multi-lag estimators by least-squares Gaussian fitting of ACFs and CCFs. Moreover, multi-lag estimators are prone to saturate at large SWs, so a hybrid estimator combines the traditional estimator and the multi-lag estimators by comparing the predicted SWs with the SW thresholds. The predicted SWs are calculated by the R0/R2 estimator, and the standard deviation (SD) of the R0/R2 estimator is introduced to identify saturated SWs. SW thresholds at different SNRs and correlation coefficients (CCs) are obtained by minimizing the evaluation metric, which is the weighted sum of biases, SDs, and invalid estimate ratios. Finally, the hybrid estimator is validated via Ku-band weather radar data (surveillance scan mode). The results show that compared with the traditional estimator, the hybrid estimator reduces biases and SDs by about$0.1\sim 0.2$m/s at SNR$0.5\sim 1$m/s using the hybrid estimator when the radar operates in the Doppler scan mode.

Predicting aerial animal migration is of great significance for biological research, ecological conservation, and agricultural production. The mechanism of animal migration is deeply coupled with spatiotemporal and meteorological factors. However, the existing large-scale prediction models using weather radar isolate the spatiotemporal characteristics and the meteorological factors. Additionally, their long-term prediction capabilities are limited, posing challenges in accurately forecasting long-term migration patterns to support applications, such as ecological warnings. This article introduces an aerial migration prediction neural network model combining multiple meteorological factors with weather radar data while expanding the horizon of the migration forecast to the scale of 7 days. Differentiated feature extraction methods are applied to different meteorological factors in the network. The transfer characteristics of the wind field in 2-D space are used to construct a dynamic migration model. The scalar meteorological data are encoded by entity embedding to perform feature fusion with the dynamic branch, collectively forming the forecast model that outputs future migration intensity. We validate the effectiveness of our model China weather radar network real data and reanalysis data, accurately forecasting migratory biomass within China for a horizon of up to 7 days. Moreover, our model is compared with two existing prediction models, demonstrating a maximum improvement of 14.00% in the coefficient of determination ($R^{2}$) in long-term forecast, and the visualized results highlight the predictive effectiveness for the spring and autumn seasons. In future applications, more meteorological factors should be considered and radar data from more stations should be collected to enhance the dataset.

Accurately extracting biological echoes is a fundamental prerequisite for weather radar aeroecology monitoring. However, the concurrent presence of meteorological echoes and biological echoes greatly restricts the extraction accuracy. Traditional neural network-based echo extraction algorithms rely on the spatial continuity feature of the echoes. But, the concurrent presence of multiple types of echoes will lead to the invalidation of the spatial feature and the error of boundary identification of biological echoes. To address this challenge, this study proposes a weak biological echo extraction algorithm using a superpixel technique, aimed at preserving richer biological details in adverse weather conditions. To amplify the imaging distinctions between biological and meteorological components, we design 8-D differential features for each superpixel patch on the CIELAB color space. The gradient boosting tree model is trained for biology classification in handling complex data scenarios. Trained trees exhibit strong generalization capabilities and imbalanced testing data that reflect real weather conditions. To mitigate the limitations posed by the lack of publicly available datasets, we establish a trainable weather radar image dataset encompassing typical weather conditions across national weather radar stations. Experimental results validated that the algorithm retains over 98% of biological data under adverse weather conditions.

Spaceborne synthetic aperture radar differential interferometry (D-InSAR) can measure large-scale surface deformation. When the deformation is negligible, the interferometric phase can be used to estimate the differential tropospheric delay (DTD). Nevertheless, a single satellite can only obtain the integrated DTD along the line-of-sight direction. Spaceborne simultaneous multiangle synthetic aperture radar (SSMA-SAR) observes a scene by multiangle spaceborne SAR satellites at the same time. It can measure integrated DTD from different viewing angles by D-InSAR, which will help realize the tomographic inversion of differential tropospheric refractivity (DTR) to obtain its spatial 3-D distribution. However, the inversion performance is sensitive to phase errors in interferograms, troposphere conditions, and system configurations. To address these issues, this article establishes an SSMA-SAR tropospheric tomography model, analyzes the error sources in the inversion, and proposes an optimization configuration design method for SSMA-SAR tropospheric tomography based on the nondominated sorting genetic algorithm II (NSGA-II). The simulation results show that SSMA-SAR has good potential to achieve high-accuracy 3-D DTR, with more than 70% improvement with the optimized configuration. Within the north latitude range of 0°–55°, the system can obtain high-precision 3-D DTR measurements and achieves submillimeter integrated tropospheric delay accuracy in the zenith direction with subkilometer resolution.

Owing to the independence of the specific differential phaseKDPfrom attenuation and miscalibration, it is often applied in attenuation estimation (i.e. estimation of the specific attenuationAfromKDP). However, the measured differential phase ΨDPincludes the backscattering differential phase δhvand noise, decreasing the accuracy ofKDPestimation. Additionally, considering that the coefficient γ of theA-KDPempirical relation is sensitive to temperature and drop size distribution, there is also concern over ana priorifixed value for γ. Especially at higher-frequency bands such as X-band, largeKDPcan amplify the biases caused by inaccurate γ. AHR and ZPHI are methods for estimating phase and attenuation respectively. Their opposite inputs and outputs create a strong coupling between the two methods, allowing both methods to simultaneously reduce the impact of contaminated ΨDPon estimation. Meanwhile, their high resolution makes it possible to conditioning γ adaptively at range resolution scale according to different rainfall situations. In this work, a method that combines the individual and common characteristics of AHR and ZPHI is proposed to improve the accuracy of phase and attenuation estimation by adaptively conditioning γ at high resolution, as well as reducing the biases of δhvand noise. The improved effects of this method are assessed with a typical storm event observed by a polarimetric X-band weather radar in Netherlands, and its performances are further evaluated with simulations comprehensively. The results show that this method can improve accuracy of both phase and attenuation estimation significantly, especially at largeKDP.

Weather radar serves as a crucial tool for monitoring aeroecology by enabling the observation of migrating birds and insects. Although dual-polarization weather radar offers the possibility of classifying echoes, extracting migration echoes of birds and insects from historical single-polarization weather radar data remains challenging. The current deep-learning methods have been successfully extracting aerial migrations from single-polarization weather radar data. However, it still faces challenges in distinguishing between birds and insects at the pixel level, primarily due to the absence of distinct semantic features for each. To tackle this challenge, we propose a semi-supervised radar data processing framework, which generates a large number of single polarization training datasets from a small amount of dual polarization truth data and trains the image segmentation network of single polarization data to distinguish between bird and insect echoes. The framework comprises three components: an image classifier, an image generator, and an image segmentation model. Specifically, the image classifier and image generator leverage a small set of manually annotated dual-polarization radar data to generate the pixel-level single-polarization dataset for training the image segmentation model. The well-trained image segmentation model extracts migration echoes of birds and insects from radar images. Experimental results demonstrate that the proposed method achieves a mean intersection over union (IoU) of 97% for segmenting precipitation, bird, and insect targets. The proposed framework can utilize historical archived single-polarization weather radar data to provide large-scale, long-term, and repeatable monitoring data for birds and insects.

Communal roosts are essential stopover sites for migratory birds. Monitoring the diurnal activity patterns of birds in communal roosts (DAPBCRs) is crucial for understanding their migratory behavior and ecological needs. This information is crucial for guiding habitat conservation and management strategies and assessing the impact of environmental changes on bird populations. Traditional methods for extracting DAPBCR often rely on detecting high reflectivity factor arc features generated by birds collectively leaving or returning to communal roosts using weather radar data and deep learning target detection algorithms. However, many bird echoes do not produce these high reflectivity factor arc features, making pixel-level extraction of DAPBCR challenging. To address this, we propose a method for DAPBCR extraction based on the differences in the probability distribution function (pdf) of differential backscattering phase between birds and insects. This method first removes nonbiological echoes from polarimetric weather radar data, retaining only biological echoes. By calculating the differential backscattering phase using the differential phase and system differential phase, we obtain the pdf of the differential backscattering phase for biological echoes. We fit this pdf to a mixed von Mises distribution to obtain the PDFs for birds and insects. Using posterior probabilities for birds and insects, we estimate the bird-insect mixing ratio and further estimate the number of birds by combining the reflectivity factor and mean radar cross section (RCS) of birds. Applying the proposed method, we extracted DAPBCR data in the midsection of the Huai River Basin near Fuyang City from June to October 2021. We found that bird activity peaked in August and September. Based on normalized cumulative bird activity, we estimated the start, peak, and end times of DAPBCR to be July 6, August 19, and September 30, respectively.

Interferometric synthetic aperture radar (InSAR) has been a valuable tool for mapping topography and subtle deformations. However, dealing with a wide imaging integral angle (IIA), especially for low-frequency band unmanned aerial vehicle (UAV) InSAR systems, introduces challenges. The conventional interferometric phase model depends on the difference in two slant ranges between the synthetic aperture centers and the target in two observations. Accuracy limitations emerge when variations are encountered in differences of slant range history across the entire wide IIA. This article explores the impact of IIA on interferometric measurements and proposes a modified interferometric phase model to address these limitations. For a wide IIA, the proposed model focuses on the integral of differences in slant range history throughout IIA by considering the nonlinear trajectory of the UAV platform. Additionally, measurement models for the IIA are deduced, in which an additional scale factor expanded by the Bessel function is introduced. Simulated and experimental datasets are utilized to demonstrate improvements in the accuracy of topography and deformation measurements. These results validate the effectiveness of the modified model in overcoming the challenges posed by wide IIAs in UAV InSAR systems.

Radar echo signals may experience the significant 2-D space dependence when it comes to the nonlinear trajectory of the synthetic aperture radar (SAR). The backprojection (BP) imaging algorithm is generally effective for achieving satisfactory focused SAR images under this condition. However, the conventional BP algorithm usually selects a uniform reference imaging plane, regardless of the actual topography of the observation scene. In undulating topographies, it has been proved that the range migration of the actual target and its projection point on the reference imaging plane may not remain consistent, leading to residual uncompensated phase errors and resulting in imaging defocusing during nonlinear trajectories. To address this problem, this article proposes an improved imaging method that involves the optimal topographic imaging plane reconstruction based on the image quality evaluation. The coarse plane and subsequent partitioned subplanes are sequentially constructed to create a topographic imaging plane that closely resembles the digital elevation model (DEM). The BP imaging algorithm is then applied to the reconstructed topographic imaging plane to overcome the defocusing problem. Both simulated and actual experiment datasets validate the effectiveness of the proposed method. Moreover, the proposed method significantly alleviates registration difficulties.

Insect morphological parameters, including mass and length, are crucial for species identification. Entomological radar can estimate morphological parameters by establishing mappings from insect radar cross section (RCS) estimators to them. Current high-accuracy methods relying on absolute RCS estimators are sensitive to echo intensity. When applied to radars without angle measurement capability, these methods may underestimate morphological parameters. This underestimation arises from the inability of such radars to compensate for reduced echo intensity caused by insects deviating from the beam center. A method using single-frequency echo intensity-independent estimators (EIIEs) was attempted; however, it could only estimate the mass of insects below 200 mg with limited accuracy. This article explores the use of multifrequency EIIEs (MFEIIEs) to enhance the estimation of insect mass and length. Based on the multifrequency scattering dataset for 159 insects measured in an anechoic chamber, the insect multifrequency scattering matrix (SM) was studied. The study revealed that four EIIEs, including the amplitude ratio and phase difference of SM eigenvalues, and two relative RCS features related to the shape of the insect polarization pattern, were correlated with insect mass and length with varied correlations with frequency. Subsequently, morphological parameter estimation was achieved by establishing the mappings from MFEIIEs to mass and length using a random forest algorithm. The presented dataset demonstrated that this method was suitable for insects below 1000 mg. Finally, the method’s effectiveness and robustness were demonstrated through field measurements on 160 insects, which yielded mean relative estimation errors of 21.82% for mass and 13.18% for length.

The accurate identification of migratory insect species is pivotal for effective pest forecasting and control strategies. Radar entomology continues to face challenges in insect identification, prompting the exploration of innovative solutions. The precision of conventional insect identification methodologies relying on morphological parameters or radar cross section (RCS) shape was inherently constrained. This study employed ensemble learning algorithms, utilizing multiple electromagnetic scattering parameters of insects as features for species classification, thereby enhancing radar’s capability to identify insects. Experiments of measuring insects using two unmanned aerial vehicles (UAVs) were carried out, aiming to establish an electromagnetic scattering database. Data were collected using a multifrequency fully polarimetric entomological radar, capturing echoes from nine major migratory pests in mainland China. Extracting the insect scattering matrix (SM) yielded a total of 22 electromagnetic scattering features categorized into four classes. Three ensemble learning algorithms were employed for classification: random forest (RF), extreme gradient boosting (XGBoost), and stacked generalization (SG). The results demonstrated that the model trained with the XGBoost algorithm consistently exhibited outstanding performance across various frequencies. In the X-band (9.5 GHz, typical operating frequency of entomological radar), the proposed XGBoost algorithm achieved an average identification accuracy of 87.50% for the nine pest species, which is approximately 13% higher than the traditional identification methods based on body size parameters. This study validated the feasibility of insect species identification based on electromagnetic scattering parameters, offering promising prospects for radar entomology to overcome challenges in insect identification.

Recent advances in deep learning have significantly improved the quality of precipitation nowcasting. Current approaches are either based on deterministic or generative models. Deterministic models perceive nowcasting as a spatiotemporal prediction task, relying on distance functions like L2-norm loss for training. While improving meteorological evaluation metrics, they inevitably produce blurry predictions with no reference value. In contrast, generative models aim to capture realistic precipitation distributions and generate nowcasting products by sampling within these distributions. However, designing a generative model that produces realistic samples satisfying meteorological evaluation indexes in real-time remains challenging, given the triple dilemma of generative learning: achieving high sample quality, mode coverage, and fast sampling simultaneously. Recently, diffusion models exhibit impressive sample quality but suffer from time-consuming sampling, severely hindering their application in nowcasting. Moreover, samples generated by the U-Net denoiser of current denoising diffusion model are prone to yield poor meteorological evaluation metrics such as CSI. To this end, we propose a spatiotemporal Transformer-based conditional diffusion model with rapid diffusion strategy. Concretely, we incorporate an adversarial mapping-based rapid diffusion strategy to overcome the time-consuming sampling process for standard diffusion models, enabling timely nowcasting. Additionally, a meticulously designed spatiotemporal Transformer-based denoiser is incorporated into diffusion models, remedying the defects in U-Net denoisers by estimating diffusion scores and improving nowcasting skill scores. Case studies of typical weather events such as thunderstorms, as well as quantitative indicators, demonstrate the effectiveness of the proposed method in generating sharper and more precise precipitation forecasts while maintaining satisfied meteorological evaluation metrics.

Deep learning (DL)-based precipitation nowcasting algorithms have garnered significant attention in recent years. However, the presence of variable spatial scales in precipitation patterns poses challenges for methods that solely focus on capturing spatiotemporal correlations at a single scale. Moreover, current DL-based algorithms tend to model short-term (e.g., 10-min time span) rainfall locally neglecting long-term, global (e.g., 2-h time span) life-cycle evolution. Furthermore, widely used pixel-wise losses are prone to produce low effective-spatial-resolution predictions. To this end, we introduce a multiscale deformable transformer network to leverage echo contexts from image patches of varying spatial scales. Meanwhile, a multihead deformable self-attention mechanism is introduced for capturing precipitation spatiotemporal dynamics in a global manner. Moreover, to improve the spatial resolution of predictions, the Fourier space regularization and adversarial losses are proposed by narrowing the discrepancy of the Fourier spectra of predictions and references. Thanks to the introduced loss function, our model generates highly effective spatial-resolution predictions with abundant details. Extensive experiments on two real datasets show the substantial superiority of our method in terms of critical success index (CSI) compared to recent competitive approaches. At the same time, our predictions have more realistic precipitation details and significantly better fidelity. For example, on a vertically integrated liquid (VIL) product dataset, compared to baseline methods, our approach reduces the Fréchet inception distance (FID) value by a factor of$2\sim 4$while improves the CSI score by 3%~5% approximately.

This paper proposes an enhanced framework for reconstructing three-dimensional (3-D) building model from airborne tomographic synthetic aperture radar (TomoSAR) point clouds which involves four crucial steps: progressive facade detection, extraction of roof points, extraction of roof outlines and reconstruction. Firstly, an efficient and robust building facade detection is undertaken by adopting a progressive detection method. Then, to extract roof points, the region growing procedure is improved by utilizing an adaptive neighborhood and robust normals of points. Subsequently, the α-shape algorithm is executed and enhanced through Delaunay triangulation network to extract fine outline points. Finally, roof outlines are regularized, and the building models get reconstructed. The proposed approach is tested using airborne TomoSAR point clouds in the Emei area located in the Sichuan province of China. The results show that the proposed method can reconstruct 3-D building models with better shapes compared to classical methods.

Space-surface bistatic synthetic aperture radar (SS-BSAR) system has the advantage of diverse observation angles due to flexible receiving configuration, thus it plays an important role in SAR multi-angle imaging and three-dimensional deformation retrieval. In this paper, based on the LuTan-1 SAR launched in 2022, we present a SS-BSAR interferometry experiment. First, the SS-BSAR system implementation and some experiment parameters are shown. Second, we introduce the SS-BSAR synchronization scheme and its imaging methods applied in this experiment, and establish a SS-BSAR dual-antenna interferometry model. Finally, we present the imaging and interferometry results of our SS-BSAR system and analyze the experimental result. This is the first result of LuTan-1 SS-BSAR interferometry, which demonstrates the ability for remote sensing observation applications based on the SS-BSAR system.

Geosynchronous Synthetic Aperture Radar(GEO SAR) has become a hot spot because of short revisit time and wide coverage. Compared with single satellite, distributed GEO SAR provides rich observation angles which makes high-accuracy three-dimensional(3D) deformation retrieval possible. However, there are significant differences in the resolution and texture of Interferometric Synthetic Aperture Radar(InSAR) image at different observation angles, which will lead to reduced accuracy of 3D deformation retrieval. In terms of problems above, Pseudo-CycleGAN is proposed in this paper based on phase unwrapping Deep Neural Network(DNN) and CycleGan. It can improve the accuracy of 3D deformation retrieval through texture assimilation of interferogram with high phase accuracy.

Designing repeat ground track (RGT) synthetic aperture radar (SAR) constellations for achieving rapid revisits over key areas is essential to employ spaceborne differential interferometric synthetic aperture radar (D-InSAR) technology in Earth observation missions such as geological disaster monitoring and prediction. In this paper, the features of average revisit time (ART) maps are first introduced and investigated, and then an efficient and resource-friendly approach to calculate the ART of constellations is proposed. On this basis, a systematic method for designing an RGT constellation is provided, incorporating lookup-table-based optimization. Once the requirements of the expected RGT constellation, the incident angle of sensors on the constellation, and the orbital elements of the seed satellite in the constellation are given, the range of the optimal inclination and longitude of the ascending node (LAN) of the seed satellite can be found and then the entire constellation is determined. The proposed method enhances the efficiency of revisit time analysis and avoids the repeated modeling when the observation requirements change. Therefore, it is applicable not only prior to launch but also guides orbital maneuvering to adjust constellation configuration for an effective response to sudden disasters, etc. Finally, multiple RGT constellation design tasks are presented to demonstrate the proposed method.

Insect mass could be estimated by estimators calculated by the radar cross-section (RCS) measured by entomological radar, which is essential for the statistics of migratory biomass and classification of insects. In order to obtain the insect mass through radar, the RCS of various insects are measured in the Microwave Anechoic Chamber by a specially designed system containing dual-polarization antennas, which point at insects in the measurement process, and the mapping between RCS and the insect mass are constructed and applied to entomological radar. However, insects might deviate from beam center in practice, and the echo intensity will decrease. The decrease cannot be compensated for radar without angle measurement capability. Through the study of insect scattering matrix (SM), it is found that co-polarization estimators, such as co-polarization ratio and co-polarization phase, are echo intensity independent and correlated with insect mass. Therefore, a co-polarization estimators calculation method driven by model and data is elaborately designed, and an estimation method for insect mass is given on basis of the co-polarization estimators. Analyses present that the method is suitable for insects below 200mg, and the mean relative mass estimation error is lower than 30% in X band and 20% in Ku band. The effectiveness and robustness of this method is verified by measuring 39 individual insects with a Ku band fully polarimetric radar in field. The proposed methods provide a way to estimate insect mass for entomological radar without angle measurement capability.

With the advantages of high spatial resolution, short repeat-pass cycle, and large observation area in Ka-band distributed geosynchronous (GEO) synthetic aperture radar (SAR) systems, its interferometry (InSAR) measurement can fast retrieve high-resolution and high-accuracy digital elevation model (DEM). However, compared to low-orbit systems, it is more difficult and costly for distributed GEO SAR systems to perform tight formation flying at such a high orbit altitude, and, therefore, atmospheric effects, which bring non-stationary and non-Gaussian phase errors, should be taken into account in its repeat-pass InSAR. To address the problems above, a spatial-temporal joint particle filter-based method (ST-PF) for DEM generation by distributed GEO InSAR is proposed in the paper. The proposed ST-PF method is validated under several stationary and non-stationary atmospheric conditions through simulation experiments, and high-accuracy and high-resolution DEMs are obtained. Moreover, the ST-PF method can withstand severe non-linearity in interferometric phases, which result from a relatively small ambiguity height in Ka-band. It is also tested that the mean square error (MSE) of the retrieved DEM is consistent with the posterior Cramer-Rao bound (pCRB) of the estimation problem, showing the validity and the accuracy of the proposed ST-PF method. With various processing parameters, errors, and scene types tested, the method shows good robustness in different conditions.

Monitoring and forecasting aerial animal migration benefit biological conservation, aviation safety, and agricultural production. Due to the lack of large-scale observation data and quantitative knowledge of aerial animal migration mechanisms, it is difficult to build a numerical simulation system for migration prediction. However, the extensive deployment of weather radars makes it possible to obtain large-scale aerial migration information. Meanwhile, artificial intelligence technologies provide new insights into the modeling of complex system. In this article, we develop a deep-learning model to predict aerial migration from the perspective of spatio-temporal evolution. Specifically, an undirected graph is applied to describe the geographic structure of the weather radar network, and then graph convolution and gated recurrent unit (GRU) are combined to extract spatio-temporal features of migration information. In addition, a multi-head self-attention mechanism is applied to enhance long-term dependence. Experiments are conducted to validate the effectiveness of the proposed model on the data from the Chinese weather radar network. The results show that our model can achieve state-of-the-art performance among the competing methods. Moreover, improvements from graph convolution and multi-head self-attention are also analyzed. In future applications, more weather radar data will be collected to enrich the dataset and build an aerial migration monitoring and prediction system.

S-band spaceborne synthetic aperture radar (SAR) systems were relatively rare since most launched missions worked in L-, C- or X-bands. An S-band spaceborne SAR should have a better interferometric coherence and penetration capability compared to a C-band spaceborne SAR, which is not fully known. In this paper, we analyze the interferometric coherence based on NovaSAR-1 data in a case in Peth, Australia. Typical regions of interest (ROIs) were selected and the performance was compared to C-band Sentinel-1 data. The results show that S-band spaceborne SARs have better penetration towards shallow and sparse vegetation regions and higher temporal coherence than C-band systems.

The errors of time and frequency synchronization in bistatic synthetic aperture radar (SAR) can have serious impacts on SAR images, if not compensated well. In this paper, we analyze the influence of time synchronization error on bistatic geosynchronous SAR (GEO SAR) imaging. First, the GEO SAR echo signal model under the influence of time synchronization error is established, through which the impacts of the constant, linear and random time synchronization errors on imaging are analyzed theoretically. Second, the theoretical analyses were verified by simulations. In addition, through simulations, the influences of the time synchronization error on low earth orbit SAR (LEO SAR) and GEO SAR are compared. The results show that the impacts of the constant, linear and random terms on GEO SAR are the same, greater and slighter compared with those on LEO SAR, respectively. This study provides a theoretical basis for future research on the time synchronization technology of multi static GEO SAR.

Traditional interferometric synthetic aperture radar (InSAR) is based on broadside looking geometry and parallel tracks. With the increase of the orbit height in spaceborne SAR and the development of SAR constellations, InSAR data of a region can be acquired in complex geometry, especially squint beam steering and unparallel tracks. For the sake of optimal InSAR system design and data processing, it is necessary to model the geometric decorrelation in complex geometry. This letter derives an accurate analytical model of geometric decorrelation of SAR interferometric pairs for general SAR observation geometry. Nonidentity of impulse responses and nonorthogonal sidelobes are the main features hindering the model derivation in the complex geometry case. An impulse response-fitting method is proposed, where nonorthogonal bases are adopted to suit the features and, thus, accurately analyze the geometric decorrelation. Simulation results verify the analytical model. It is found that unparallel tracks will introduce an extra geometric decorrelation factor. Compared to cases of parallel tracks, unparallel tracks always worsen the geometric decorrelation and cannot be neglected.

Fast observations of rapid surface large-changes are demanded in disaster evaluations and scientific studies. Digital elevation model (DEM) differencing before and after the events is an effective way to retrieve the changes. Owing to a short repeat cycle, geosynchronous synthetic aperture radar (GEO SAR) systems can quickly obtain repeat-pass data and generate postevent DEMs by interferometry. However, interferometric baselines under its quick revisit cases are short, resulting in generating low-accuracy postevent DEMs. Moreover, surface large-changes can bring height ambiguity problems under the single-baseline interferometric processing. In this letter, we address the problem through a multibaseline (MB) processing. Since GEO SAR MB data can derive from the repeat-pass interferometric data of different subapertures and revisits, a subaperture-decomposition-based temporal and spatial MB method is proposed. The simulation results verify the effectiveness of the proposed method, where the quickly generated postevent DEM can help to realize the rapid large-elevation change observations.

The ability of insect orientation measurement of entomological radars supported the study of insect heading behaviors. However, the current entomological radars can only measure the two-dimension (2D) orientation that is the projection of the three-dimension (3D) orientation on the horizontal plane. The ability to measure the 3D orientation of insect will promote the study of the vertical and 3D heading behaviors of insect. In this study, an insect 3D orientation estimation method based on cooperative observation from two views of entomological radars is proposed. The expression of 3D orientation is deduced based on 2D orientation, azimuth and elevation measured with two radars at different stations through vector operation. The simulations are conducted to verify the effectiveness of the proposed method. The result shows that the estimation error of 3D orientation depends on that of the 2D orientation and the included angle between two lines of sight of two radars. A multi-aspect fully polarimetric rig is designed to measure insect echo signals from two aspects in a microwave anechoic chamber. The experiment data is used to further validate the effectiveness of the proposed method.

Ground-based multiple-input multiple-output (GB-MIMO) radar is capable of performing structural health monitoring through vibration monitoring. However, when a target’s vibration frequency is comparable to the image acquisition frequency of MIMO radar, its vibration can affect the vibration parameters estimation based on the pixel’s phase series. In this letter, the impact of target vibration on the pixel series is analyzed. A robust method for vibration parameter estimation is proposed by operating iteration between the series compensation and estimation. The correctness and effectiveness of proposed method is verified by both simulation and calibrator experiments.

Time-series interferograms acquired on a deep pit with a Ground-Based Synthetic Aperture Radar (GB-SAR) system showed that the atmospheric phase (AP) could be complexly space variant due to rapid changes of the weather conditions and steep topography. Conventional compensation methods that simulate the AP with typical parametrical models are no longer applicable. Based on the theoretical path integral model of the AP, a grid partition (GP) method is proposed. By dividing one interferogram into a certain number of small grids, the refraction variation inside each grid is assumed to be a constant. A system of linear equations is first built based on sufficient permanent scatterers (PSs). Then, bounds and inequality constraints are set to limit the refraction variation of each grid. A constrained linear least-square problem is solved with two-step process to estimate and compensate the AP. To fully validate the feasibility of the GP method, simulated phase interferograms based on four conventional AP models and with the consideration of deformation areas and noise phase are first processed. Then, four experimental interferograms with different types of AP components are processed and made comparisons with the conventional parametrical methods. The quantitative comparisons of the simulated and experimental data sets both proved that the GP method can well reduce the AP errors.

Doppler weather radar observations of strong convective weather phenomena, e.g., tornadoes and supercells, showed that their Doppler spectra could deviate from the Gaussian shape. Classical method for identification of non-Gaussian spectral signal (NGSS) is based on spectral width and high-order spectral moments (HOSM). The DFT processing is needed to obtain the power spectrum density to calculate HOSMs, increasing the computation complexity. Besides, power, mean velocity, and spectral width are often obtained using autocorrelation method, i.e., pulse-pair-processing (PPP) method which is generally considered to be the most efficient estimator, where a Gaussian spectrum is assumed. Thus, a bias in the mean velocity and spectral width will occur if the Doppler spectrum deviates from the Gaussian shape. In this article, a generalized PPP (GPPP) method, which calculates HOSMs using autocorrelation function directly, is proposed. It outputs two additional identification parameter products, including skewness and kurtosis, which can directly identify NGSS and contribute to select the appropriate spectral moment estimation algorithm for Gaussian-or-not spectral signal. Compared with the classical method, the NGSS identification based on the GPPP method has a lower computation complexity since the DFT processing is avoided and has a better performance especially for low signal-to-noise ratio conditions. Besides, the clustering algorithms including expectation maximization and K-means algorithms are investigated for the adaptive spectral moment estimation of NGSS for the first time. The numerical simulation experiments and verifications based on real weather radar data of an actual supercell are implemented to confirm the feasibility and superiority of proposed method.

Nowadays deep learning based weather radar echo extrapolation methods have competently improved nowcasting quality. Current pure convolutional or convolutional recurrent neural network based extrapolation pipelines inherently struggle in capturing both global and local spatiotemporal interactions simultaneously, thereby limiting nowcasting performances, e.g., they not only tend to underestimate heavy rainfalls’ spatial coverage and intensity but also fail to precisely predict non-linear motion patterns. Furthermore, the usually adopted pixel-wise objective functions lead to blurry predictions. To this end, we propose a novel motion-guided global-local aggregation Transformer network for effectively combining spatiotemporal cues at different time scales, thereby strengthening global-local spatiotemporal aggregation urgently required by the extrapolation task. First, we divide existing observations into both short and long term sequences to represent echo dynamics at different time scales. Then, to introduce reasonable motion guidance to Transformer, we customize an end-to-end module for jointly extracting Motion Representation of Short and Long term echo sequences (MRS, MRL), while estimating optical flow. Subsequently, based on Transformer architecture, MRS is used as queries to retrospect the most useful information from MRL for an effective aggregation of global long-term and local short-term cues. Finally, the fused feature is employed for future echo prediction. Additionally, for the blurry prediction problem, predictions from our model trained with an adversarial regularization achieve superior performances not only in nowcasting skill scores but also in precipitation details and image clarity over existing methods. Extensive experiments on two challenging radar echo datasets demonstrate the effectiveness of our proposed method.

Polarization information can greatly improve the ability of detection, parameter retrieval and classification for entomological radar. In recent, the instantaneous fully polarimetric measurement technique is used in entomological radar, so as to relax restriction of invariant orientation during observation compared to the typical vertical-looking radar with linear-polarized rotation configuration. However, the fully polarimetric measurement is based on a multi-channels radar system, and the imbalance and cross-talk between channels, namely polarimetric errors, will severely affect the measurement of the polarimetric scattering matrix (PSM). So, polarimetric calibration is essential to obtain the accurate polarization information of targets. In contrast to inefficient and manual calibration methods, a data-driven polarimetric calibration method is proposed based on the assumption of reciprocity and bilateral symmetry of insects in this paper. The polarimetric errors are elaborately decomposed into two components which could be estimated by reciprocity and bilateral symmetry, respectively. In addition, because the proposed method is data-driven, the calibration process could be automatically executed to compensate the temporal-variant polarimetric errors induced by temperature change, and thus suitable for long-term operation of entomological radar. Simulations and experiments are carried out to evaluate the performance of the proposed calibration method. Results show that it could achieve high accuracy when the number of insects used as calibrators is large enough. The proposed method has been applied in Ku-band high-resolution fully polarimetric entomological radars for cross-border migratory insect observation in Yunnan province, China.

For insect radar observations, exploiting radar echoes from insects to accurately estimate size parameters such as body mass, length, and width of insects can help to identify insect species. At present, the commonly used method for estimating insect body size parameters in insect radar is to use the monotonic mapping relationship between insect RCS parameters of a single frequency (mainly 9.4 GHz) and body size, and obtain the empirical formula for body size estimation by polynomial fitting. However, the useful information used by the traditional methods is limited (1 to 2 features), and these retrieval methods are simple and with limited estimation accuracy. This paper proposed a feature-selection-based machine learning method for insect body size estimation, which could effectively improve the body size parameter estimation accuracy of insect radars. First of all, based on the published insect scattering dataset (9.4GHz, 366 specimens of 76 species), stepwise regression was used to select the optimal feature combinations for body size estimation, then three machine learning methods, Random Forest Regression (RFR), Support Vector Regression (SVR) and Multilayer Perceptron (MLP), were adopted to achieve estimation of insect body size. Among them, RFR has the best performance (mass 18.83%, length 11.37%, width 16.87%). Subsequently, based on the measured dataset of migratory insects (5532 specimens of 23 species), the influence of the estimation error of insect body size on the identification accuracy of migratory insect species was analyzed. When incorporating the estimation error of the feature-selection-based RFR method, the insect identification rate of 83.68% was reached.

Vertical-looking radar (VLR) is a significant milestone in the development of insect radars with the capability of detecting the behavior of migratory insects and their biological parameters. In current VLRs, high-speed continuous sampling and long-time integration can barely be performed simultaneously, leading to a low detection probability for tiny insects (weight < 10 mg). Based on the large amount of data acquired by our developed high-range resolution insect radar, the insect echo signals and vertical motion characteristics are initially analyzed and demonstrate that the linear-motion mode is dominant in insect migration; also, the echo signal power of most insects follows the gamma distribution. Based on these characteristics, a long-time integration and detection method for detecting migratory insects, especially tiny targets from echo signals that often dip below the noise level, is proposed. The radial target velocity is also measured as one of the output parameters. The theoretical derivation and optimal choice of detection thresholds are also presented. Simulation and experimental results demonstrate that the proposed method exhibits better insect detection performance and effectively increases the detection range compared with conventional methods. In addition, the measured target velocity can be directly applied to current continuous-sampling VLRs for the ascent and descent rate analysis. Many typical insect migration phenomena have been detected effectively utilizing our developed VLR, and the measured ascent and descent rates of insects agree well with typical take-off, cruising, and landing behaviors. This is the first reported successful VLR application on take-off and landing behaviors of migratory tiny and dense insects.

Dynamic deformation measurement is an important approach to monitor the structure stability. Due to its rapid imaging capabilities, ground-based multi-input multi-output (GB-MIMO) radar has shown great application potential in bridge structure monitoring. This paper proposes a dynamic deformation estimation method based on radar image series. Firstly, an improved clutter suppression method is utilized to overcome the estimation error in complex working status. Secondly, a two-step pixel extraction method is adopted to ensure both quantity and quality of sample pixels. Finally, frequency- and time-domain environmental stimulating methods are jointly considered for stable mode shape estimation. This paper systematically measured the dynamic deformations of bridge structures, including two suspension bridges and a cable-stayed bridge. The time-series deformation, deflection, vibration frequency and amplitude on the bridge structure are obtained by image domain measurement. And for the first time, the mode shapes of bridges are obtained through GB-MIMO radar. The correctness of structure parameter measurement is verified with the finite element model (FEM). Experimental results prove that with the proposed method, stable results could be acquired against weak and complex stimulation conditions.

A long aperture time is required to achieve a high signal-to-noise ratio and high azimuth resolution in geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO SA-BSAR) system for moving target indication (MTI). The range walk migration because of the target's motion cannot be ignored for such a long time, and the second-order range model fails for the moving target. In this paper, an adaptive MTI method is proposed for GEO SA-BSAR with a long aperture time, ensuring the moving target's detection and location. Firstly, an adaptive spatial filter modified by the accurate GEO SA-BSAR multichannel signal model is applied to clutter suppression and beamforming. Next, we adopt the generalized Radon-Fourier transform to maximize the signal-to-noise ratio of the moving target with unknown motion parameters. Then, the moving target can be detected, and its position has been obtained. Finally, the simulation experiments are conducted to show the effectiveness of our technique.

Geosynchronous Synthetic Aperture Radar (GEO SAR) has advantages of a short revisit time and large coverage for the scene of interest, so lots of theories and analysis toward the GEO SAR have been developed. However, GEO SAR systems may generate radio frequency interference (RFI) to a low earth orbit SAR (LEO SAR), causing a decrease in Signal-to-Interference-plus-Noise Ratio (SINR) of SAR images. In order to evaluate the GEO-to-LEO RFI effect on imaging, we deduce the formulas of the RFI power and image SINR, and verify them by comparing them with numerically evaluated results from simulated images. Based on the formulas, we evaluate SINRs of LEO SAR images for different bistatic scattering coefficients. The results show that when the target forms a specular bistatic scattering geometric relationship with a GEO SAR and a LEO SAR, LEO SAR image quality is poor, with a SINR worse than 5 dB, but the RFI effects can be neglected in other cases.

An experiment which involves the simultaneous deployment of three ground-based multiple-input multiple-output (GB-MIMO) radar systems to measure 3-D deformation of a displaceable corner reflector (DCR) is outlined in this letter. The DCR successively displaces in three mutually orthogonal directions and each radar measures 1-D deformation independently. Since the displacement directions of the DCR cannot be measured, they are estimated by solving a nonlinear equation set based on the rotation relationship between two 3-D coordinate systems, whose effectiveness is verified by simulation. Considering that the measurement accuracies of the DCR’s displacements along three axial directions of a 3-D coordinate system, that is, 3-D deformation, are related with how the coordinate system is built, the geometric dilution of precision (GDOP) is then utilized to take accuracy analysis. The measured and theoretical GDOP are rather close, which validates the feasibility of 3-D deformation measurement with three radar systems.

Coherence-based synthetic aperture radar (SAR) tomography (TomoSAR) exploits the complex coherences of SAR images to achieve 3-D imaging. Utilizing two-sensor spaceborne SAR formation flying to realize coherence-based TomoSAR has attracted increasing attention because temporal decorrelation-free interferograms can be constructed; therefore, TomoSAR has excellent potential for inverting the vertical structures of natural scenes such as forests and glaciers. However, low earth orbit (LEO) TomoSAR is disadvantaged by limited data and nonuniform sampling in the elevation direction. Geosynchronous (GEO) TomoSAR can overcome these limitations owing to its short revisit time of no more than 24 h. For the first time, this article discusses coherence-based TomoSAR exploiting GEO SAR formation flying. The benefits of GEO-formation coherence-based TomoSAR, including the low cost of slave satellites, rich data sets, and uniform sampling, are noted. The key problems of system design, including the formation design and data acquisition, are discussed. A formation design method based on the minimum along-track baseline is proposed that can realize uniform elevation sampling. The geometric correlation of a general SAR observation geometry is derived; on this basis, an optimal data acquisition method based on the optimal height measurement Cramer-Rao lower bound (CRLB) is proposed. Finally, the performance of GEO-formation coherence-based TomoSAR is analyzed; in particular, the ambiguity height in the altitude direction, the Rayleigh resolution in the altitude direction, and the theoretical optimal geometric correlation are evaluated. Finally, computer simulations validate the proposed formation design method, data acquisition scheme, and performance analysis formula.

The measurement of insect radar cross section (RCS) is a prerequisite for the studies such as the quantitative estimation of insect population density and the identification of insects using entomological radar. In this article, we established a multiband polarimetric RCS measurement system in the microwave anechoic chamber. The targets’ range profile at different frequencies can be obtained based on the step frequency continuous wave, and meanwhile the clutter elimination and polarimetric calibration were applied to reduce the measuring error. The multifrequency (X-/Ku-/Ka-bands) polarimetric RCSs of 169 insects belonging to 21 species were measured and reported, which is the first time to systematically present the multifrequency polarimetric RCSs of insects. The mass of all specimens range from 25.6 to 964 mg, and their ventral-aspect RCSs range from −57.47 to −32.17 dBsm at X-band, from −48.27 to −33.87 dBsm at Ku-band and from −69.76 to −36.40 dBsm at Ka-band. For small insects less than 300 mg, the HH polarization RCS increases rapidly with frequency at X-band and fluctuates with the frequency at Ku-band, while the VV polarization RCS increases monotonically with frequency at X- and Ku-band. For larger insects, the HH polarization RCS decreased slowly with frequency at X-band and fluctuates with the frequency at Ku-band, while the VV polarization RCS increases with the frequency, then reaches the maximum, finally fluctuates with the frequency. At Ka-band, the measured polarization RCS versus frequency curves are smooth and all show similar variation. The measurement results verify the effectiveness and accuracy of the established system.

Geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO SA-BSAR), consisting of GEO transmitter and airborne receiver, has stable coverage for a long time and benefits moving target detection. The performance of GEO SA-BSAR moving target indication (GEO SA-BSAR MTI) system is investigated. This work provides the first analytical connection between target detection performance and configuration parameters based on the optimum output signal to clutter and noise ratio (SCNR) criterion. Furthermore, a theoretical analysis of motion parameter estimation is stated in terms of the Cramer-Rao lower bounds (CRLB). The SCNR loss and CRLB provides a necessary design configuration for GEO SA-BSAR MTI system. Finally, the optimized configuration is provided by comparing the performance.

In this paper, we develop a simulator that could simulate polarimetric data of airship-borne typhoon observation radar. We use the Weather Research and Forecast (WRF) model to generate a `realistic' typhoon phenomenon, combined with the T-matrix method to calculate the scattering properties. Mixtures of rain, snow, graupel and ice relative to typhoon phenomenon are considered. Polarimetric observables also depend on the angle of the incident radar beam, so we analyzed the influence of incident angles on radar observables. The simulated polarimetric observables showed a good agreement with those in previous literature.

Multistatic geosynchronous synthetic aperture radar (GEO SAR) utilizes multiple satellites' transmitted and received signals simultaneously to generate multiple phase centers (PCs) and to reduce the synthetic aperture time and the power budget. Two key problems of this SAR system are the design of the PC array configuration to satisfy the resolution requirements and the suppression of the grating lobes that are inherently introduced by the PC array. The resolution of the SAR can be analyzed using generalized ambiguity functions (GAFs). However, the current research on the multistatic SAR resolution and grating lobes is mostly based on specified configurations and numerical simulation methods, which cannot establish the relationship between the system parameters and the resolution. This article proposes an analytical analysis method for the multistatic GEO SAR GAF-based on the array spatial ambiguity function (ASAF). First, gradient analysis is used to obtain analytical expressions for ASAF and the multistatic SAR GAF. On this basis, an analytical resolution expression is obtained, and an orbital element design method that considers the Earth's rotation is proposed. In addition, the lobe positions are analytically expressed based on the geometry, and the grating lobes are suppressed by designing the optimal integration time such that the null depth of the velocity ambiguity function (VAF) coincides with the grating lobe positions. Finally, simulation results at various positions for various orbit types demonstrate the accuracy of the GAF approximation formula and the satisfactory performance of the optimal time expression in suppressing the grating lobes.

Quantifying the distribution of the aerial organisms is essential for investigating the movement and behavior of migratory animals. This large-scale broad-front migration can be readily detected by weather radars. However, estimating their vertical distribution is still biased due to the vertical variability of the reflectivity in the radar beam. In this article, we establish a weather radar biological observation model and propose a retrieval method to identify the vertical profiles of reflectivity (VPRs) using regularization technique, which can eliminate the estimation bias. The performance of the method is evaluated using different radar antenna patterns and different regularization parameters, and a sensitivity analysis is performed. The improvement of the method is represented by comparing to the direct method. We apply this method to autumn migration cases over the east coast of China; the demonstration results show the potential of this method in the study of migratory animals.

Current vertical-beam entomological radars record the polarization direction corresponding to the maximal ventral-aspect radar cross section (RCS) as the insect's orientation. For so-called “parallel” insects, this direction is indeed their orientation; but for “perpendicular” insects, it is at right angles to the orientation. Current entomological radars cannot discriminate the parallel and perpendicular cases. This article shows here that discrimination is possible using the relative phase of the scattering matrix (SM) eigenvalues. Multifrequency fully polarimetric ventral aspect SM measurements of 80 insect specimens of 12 species have been made in a microwave anechoic chamber. The relationship of the polarization direction corresponding to the maximal RCS and the radar frequency has been analyzed, and from these results a method of discriminating parallel and perpendicular insects, based on the relative phase of the SM eigenvalues, is proposed. The method is applicable to X- and Ku-band observations, with a high correct-identification rate, and can be used with both fully polarimetric entomological radars and coherent rotating-polarization units, but not with the noncoherent rotating-polarization configuration used in traditional vertical-looking radars (VLRs). Finally, the performance of the method is discussed, and it is found that it has better performance for middle and large insects at X-band and small and middle insects at Ku-band.

Despite the increasing interest in geosynchronous synthetic aperture radar (GEO SAR) systems, the ground moving target indication and motion parameters estimation aspect have never been addressed in GEO SAR scenarios. In this article, we tackle the issue of multiple moving target velocity estimation in GEO SAR. We develop new closed-form expressions that relate both Doppler centroid and Doppler rate to the target motion parameters in GEO SAR by considering the specific features of a geosynchronous orbit. Furthermore, we propose a new velocity estimation algorithm that combines the nonuniform cubic phase function (NU-CPF) algorithm with the newly developed models to estimate the moving target's two velocity components. Moreover, based on the above-mentioned technique, we further propose a solution to address the multiple moving targets' problem. Simulation results along with an estimation accuracy analysis are provided to demonstrate the effectiveness of the proposed multitarget GEO SAR velocity estimation technique.

Geosynchronous spaceborne-airborne bistatic multichannel synthetic aperture radar (GEO-SABM SAR) is a form of bistatic SAR constructed by a GEO transmitter and an airborne multichannel receiver. The frequent coverage of earth observations in GEO-SABM SAR provides a great advantage in SAR-ground moving target indication (SAR-GMTI). However, the bandwidth of the azimuth spectrum in this configuration, which is larger than the pulse repetition frequency (PRF), makes the whole spectrum fold into several subsegments, i.e., azimuth spectrum aliasing (ASA) occurs. In addition, azimuth Doppler ambiguity (ADA) caused by the larger radial velocity of the moving target may occur simultaneously. To address these problems, an improved velocity SAR (VSAR)-based azimuth spectrum reconstruction method for moving targets is proposed. The influence of ASA and ADA on the signal of the moving targets is analyzed, and a signal model after coarse imaging with static target parameters is deduced. From the signal model, each subsegment has different Doppler frequencies and can be extracted based on improved VSAR. Meanwhile, the Doppler ambiguous integer has a definite relationship with the slope of the range migration of the subsegments after a range cell migration correction (RCMC) based on the static target parameters and can be estimated by this relationship. Furthermore, based on the extracted spectrum subsegments and estimated Doppler ambiguous integer, the azimuth spectrum can be reconstructed. Finally, the effectiveness of the proposed method is verified by the numerical experiments.

Geosynchronous SAR tomography (GEO TomoSAR) is a technique that combines geosynchronous SAR (GEO SAR) with SAR tomography. It can make full use of the advantages of short revisit time and large observation area of GEO SAR to achieve timely and accurate three-dimensional (3D) reconstruction of targets. However, GEO SAR is severely affected by the temporal-spatial variant ionosphere, which causes image shift and induces residual phase between repeated orbital data. These can seriously affect tomographic performance. In the paper, the GEO TomoSAR signal model affected by the ionosphere is established based on the temporal-spatial variant total electron content (TEC) model of background ionosphere. Then the influence of ionosphere on different scatterers projected into the same pixel in GEO SAR images is analyzed, and the expression of residual phase error between repeated orbit images is given. After analysis, the influence of the background ionosphere on the GEO TomoSAR system mainly includes the relative offset of the scattering target and the imaging defocus in the elevation direction. Finally, the experimental verification was completed using US-TEC data.

Geosynchronous synthetic aperture radar (GEO SAR) has great potentials in synthetic aperture radar ground moving target indication (SAR-GMTI) due to its high time resolution and wide swath coverage. But the signal-to-noise ratio (SNR) of echo is too low and multichannel configuration cannot be realize in GEO monostatic SAR. Therefore, geosynchronous spaceborne-airborne bistatic multichannel synthetic aperture radar (GEO-SABM SAR) can realize enhanced SNR and multichannel configuration. In this paper, we establish the slant range model for GEO-SABM SAR and give the VSAR processing flowchart. Finally, the numerical experiments is given to verify the effectiveness of proposed method.

For radar observations, invariant target parameters extracted from the scattering matrix (SM) provide information about the target's geometry and composition. By studying the invariant target parameters of a small sample of insects, it is shown that the two eigenvalues and the determinant of the Graves power matrix are strongly correlated with insect body length and mass. Therefore, two methods are proposed to estimate the body length and mass from the eigenvalues or the determinant. The two eigenvalues identify the maximum of the insect polarization pattern and the perpendicular to the maximum direction, while the determinant is the product of these two values. A sample of 207 insect specimens measured at X-band in the laboratory rigs is used to determine the relationships between SM parameters and body lengths and masses. The results show that the length and mass can be estimated with a good performance and without an initial classification stage. In addition, the use of the determinant of the Graves power matrix is shown to provide an improved, SM-based, method of determining insect orientation and to solve the 90° orientation-extraction error problem that arises at X-band with very large insects.

Synthetic aperture radar (SAR) tomography (TomoSAR) techniques exploit multipass acquisitions of the same scene with slightly different view angles, and allow generating fully 3-D images, providing an estimation of scatterers' distribution along range, azimuth, and elevation directions. This paper extends TomoSAR to geosynchronous SAR (GEO TomoSAR). First, the potential and performance of GEO TomoSAR were analyzed from the perspective of orbital perturbation and the resulting large spatial baseline. Then, the rotation-induced decorrelation problems induced by the along-track baseline component were analyzed. In addition, the optimized acquisition geometry and tomographic processing flow were given, and the computer simulation verification was also completed. Finally, the equivalent validation experiment based on Beidou inclined geosynchronous orbit (IGSO) navigation satellite was carried out to demonstrate the feasibility and effectiveness of GEO TomoSAR. The experimental system employs the Beidou IGSO satellite as illuminator of opportunity and a ground system collecting and processing reflected echoes. This is the first time to employ the data from repeat-track Beidou IGSO satellites for tomographic processing. The 3-D imaging of the urban area using this experimental system was presented and then verified using LiDAR cloud data as reference. The results show that GEO TomoSAR can form the baseline of the order of hundreds of kilometers in elevation, which has the ability to achieve a resolution of 5 m in elevation.

Insect migration provides major ecosystem services, and sometimes, migratory pests cause serious crop damage and yield loss. Species identification is critically important in studies of insect migration, for both entomologists and pest managers. Radar is an effective means of detecting insect migrants. Current entomological radars usually operate at X-band, and signal amplitude information is used to estimate body mass and wing-beat frequency, which can then be used to categorize migratory insects into broad taxon classes. To improve the identification performance, this paper presents a novel radar method of measuring insect mass and body length. The multifrequency radar cross sections (RCS) of insects at X-band and Ku-/K-band are fully investigated, and the comprehensive relationship between RCS and insect morphological parameters provides an improvement in the estimation of insect mass. More importantly, estimations of body length can also be realized with an accuracy of 84% based on experimental data acquired by a vector network analyzer in a microwave anechoic chamber. If multifrequency RCS measurements can be obtained by radar in the future, then highly accurate estimations of insect mass and body length will be possible, although it is currently still a challenge to build a radar capable of making the required measurements over such a wide frequency range.

In this letter, a velocity estimation method for moving ships in synthetic aperture radar (SAR) images is proposed based on a subaperture decomposition technique. In contrast to traditional methods, our method needs only a few SAR imaging parameters besides the SAR image itself. The behavior of moving ships in subaperture images is theoretically analyzed, and the ship motion parameters in azimuth direction are accurately estimated. The proposed approach was tested on real SAR stripmap images acquired by TanDEM-X, a twin satellite SAR constellation. The estimated azimuth velocities perfectly fit the data recorded by the international automatic identification system.

The passive Synthetic Aperture Radar with Global Navigation Satellite System (GNSS) employs GNSS satellites as transmitters and receivers mounted near the ground. Since GNSS constellations are designed for global, reliable and persistent operation, the most important advantage of such a system is the potential of permanent monitoring on the area which is overlooked by a fixed receiver. In the paper, an experimental prototype of GNSS-based SAR for the purpose of obtaining larger scene images as well as the development progress was introduced at the first place. And then a latest imaging experiment of GNSS-Based SAR for railway bridge was carried out and the image was obtained with high quality when it was firstly used for China Railway High-Speed (CRH) railway bridge imaging. The positive imaging result suggested that it is possible to achieve change information extraction using GNSS-based SAR for the further safety evaluation of CRH operation in China.

Geosynchronous SAR (GEO SAR) has better coverage and revisiting performance than the low Earth orbit SAR. Thus GEO SAR can implement the repeat-pass interferometry as its revisit time of 1 day and the orbit drift (forming baseline) induced by perturbation. In comparison, the twin GEO SAR formation configuration in which the slave satellite repeats the master satellite' track can realize shorter revisit time and adjustable baseline by tuning the orbit elements. In this paper, a validation experiment based on the Chinese Beidou IGSO navigation satellites is demonstrated to validate the feasibility and effectiveness of this concept. The validation experiment consists of data acquisition of the Beidou IGSO satellites signals, equivalent pre-processing, image processing and interferometry processing. In the end, we obtain the high-precision experimental results of deformation inversion, validating the feasibility of twin GEO SAR interferometry/differential interferometry.

Ground-based synthetic aperture radar (GB-SAR) technique has been widely applied for the deformation monitoring and measurement of the natural and engineered slopes. To extend the 2-D deformation measurement from the conventional 1-D measurement along the radar-target line of sight (LOS), multiple aperture interferometry (MAI) techniques based on phase differences between interferograms of the forward-looking and backward-looking subapertures are tackled in this letter. The optimal subaperture selection is analyzed considering the typical signal-to-noise ratios and correlations in GB-SAR applications. Simulations prove that the coherent integration (CIM) can be utilized to improve the measurement accuracy of the MAI method. Besides, GB-SAR experiments are carried out to validate the feasibility and effectiveness of the 2-D deformation measurement method based on MAI. Accuracy comparison of deformation measurement with the MAI and cross correlation methods is also taken. Experimental results show that the accuracy of deformation measurement along the perpendicular direction to LOS based on MAI and CIM can reach millimeter level for displaceable corner reflector.

A novel insect orientation extraction method is proposed based on the target polarization scattering matrix (PSM) estimation, which is applicable for traditional vertical-looking insect radar with noncoherent reception as well as the coherent radar. The insect echo signal at different polarization directions on the radar polarization plane is usually acquired by means of rotating linearly polarized antenna. In this letter, the insect echo signal is first used to accurately estimate insect PSM by an iterative algorithm based on the second-order polynomial approximation. Meanwhile, the Cramer-Rao lower bound is also analyzed to test the estimation performance. Next, based on the assumption that the target orientation is consistent with the dominant eigenvector, the insect orientation is extracted from the estimated PSM. Finally, both theoretical simulations and real experimental data are used to validate the effectiveness and feasibility of our proposed method, which can achieve good orientation estimation accuracy at low signal-to-noise ratio.

An L-band geosynchronous synthetic aperture radar (GEO SAR) will be inevitably affected by ionosphere scintillation because of its low carrier frequency. Meanwhile, compared with the low Earth orbit (LEO) SAR, a higher orbit of GEO SAR makes it have a longer integration time and a longer operation time within the susceptible regions of ionospheric scintillation. Thus, its imaging is more sensitive to ionospheric scintillation, and the corresponding degradation will have a different pattern. However, few works are focused on the quantitative analysis of the ionospheric scintillation impacts on L-band SAR. Moreover, the parameters of ionospheric irregularities utilized in the analyses are hard to be determined. In this paper, we first deduced the azimuth point-spread function with the consideration of both the amplitude and phase scintillation. Then, based on the measurable statistical parameters of ionospheric scintillation, performance specifications, including azimuth resolution, azimuth peak-to-sidelobe ratio (PSLR), and azimuth integrated sidelobe ratio (ISLR) are obtained to fully evaluate the impacts. The analysis suggests that in GEO SAR imaging, the azimuth ISLR severely deteriorates, whereas degradations of the azimuth resolution and PSLR are negligible. Finally, the simulations and a real ionospheric scintillation monitoring experiment by employing Global Positioning System satellites receivers were conducted, verifying the conclusions that the serious degraded contrast and focus quality of the images are brought by the raised azimuth ISLR.

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The 3-D deformation retrieval is significant for the accurate evaluation of geologic disasters (e.g., earthquakes and landslides). Multiple-aperture interferometry (MAI) is an effective method to obtain 3-D deformation, combined with the cross-heading tracks synthetic aperture radar (SAR) data. However, because of the limitations of the low earth orbit SAR, a long satellite revisit time, small common areas of the cross-heading tracks data, and the unsatisfied along-track deformation measurement accuracy usually exist in the traditional MAI 3-D deformation retrieval. Geosynchronous SAR (GEO SAR) runs in the geosynchronous orbit, which has the advantages of a large observation area and a short revisit time. This paper focuses on 3-D deformation retrieval by GEO SAR MAI processing. Aiming at the high orbit and the squint looking of GEO SAR, the accurate expressions of the along-track deformation, 3-D deformation, and the errors in GEO SAR MAI processing are given. The distortions and their correction in the MAI interferogram brought by the geometrical difference between the forward- and backward-looking interferograms and the multicycles flat-earth and topographic phases are given. Moreover, an optimal subaperture selection method based on minimum position dilution of precision is proposed. Finally, the effectiveness of the proposed method is validated by simulations and the experiment of BeiDou-2 inclined geosynchronous orbit navigation satellite. The theoretical analysis and the experimental results suggest centimeter-level and even millimeter-level deformation measurement accuracy could be obtained in 3-D by GEO SAR MAI processing.

The ionospheric scintillation induced by local ionospheric plasma anomalies could lead to significant degradation for geosynchronous earth orbit synthetic aperture radar (SAR) imaging. As radar signals pass through the ionosphere with locally variational plasma density, the signal amplitude and phase fluctuations are induced, which principally affect the azimuthal pulse response function. In this paper, the compensation of signal amplitude and phase fluctuations is studied. First, space-variance problem of scintillation is addressed by image segmentation. Then, SPECAN imaging algorithm is adopted for each image segment, because it is computationally efficient for small imaging scene. Furthermore, an iterative algorithm based on entropy minimum is derived to jointly compensate the signal amplitude and phase fluctuations. Finally, a real SAR scene simulation is used to validate our proposed method, where both the simulated scintillation using phase screen technique and the real GPS-derived scintillation data are adopted to degrade the imaging quality.

Since geosynchronous synthetic aperture radar (GEO SAR) has curved trajectories, back-projection algorithm (BPA) greatly fits for its imaging. However, for a scene with height variation, the reference range based on the fixed-height imaging grid under curved trajectories is inaccurate in azimuth back-projection. Resultantly, the GEO SAR image quality will be obviously deteriorated in high resolution imaging. To address the issue, this paper proposed the digital elevation model (DEM)-assisted BPA to realize the accurate high resolution GEO SAR imaging for the scene with height variation. DEM information is utilized to construct the imaging grid in the new method for generating the accurate reference range. Simulation results validate that the proposed method achieves good imaging performance for the scene with height variation.

The tropospheric time variance within the ultra-long integration time of Geosynchronous Synthetic Aperture Radar (GEO SAR) needs to be considered for imaging. Meanwhile, because of the curved trajectory and the very complex geometry relationship between the target position and the satellite, the traditional analysis method of troposphere on low earth orbit (LEO) SAR imaging will no longer be suitable in GEO SAR. In this paper, GEO SAR signal phase errors induced by the time variation of troposphere is obtained. Then, a GEO SAR signal's two-dimension frequency spectrum under the effects of the troposphere is derived for the first time. The GEO SAR 2-D image shift and image defocusing phase errors are derived analytically. At last, the atmospheric refractivity profile (ARP) data provided by the Chinese meteorological satellite FengYun-3 (FY-3) are used and the focusing results are presented to verify the aforementioned analysis.

L-band geosynchronous synthetic aperture radar (GEO SAR) images will most likely deteriorate in the presence of ionosphere scintillation interference due to the low carrier frequency of GEO SAR. Meanwhile, because of the high orbit and the long working time above the region with the active ionosphere, GEO SAR will experience ionospheric scintillation with a higher probability. To make the GEO SAR avoid being interfered by ionospheric scintillation, we propose an orbit-optimization strategy by utilizing the diurnal and geographical pattern of the ionospheric scintillation occurrence in this letter. As the equatorial region is likely to experience ionospheric scintillation during the specified time window from the early evening after sunset to midnight, the orbit can be optimized by tuning the GEO SAR orbit parameters (e.g., a proper time past perigee) to avoid imaging over the equatorial region during the specified time window. Finally, simulation is conducted to verify the effectiveness of the method under the proposed three types of GEO SAR orbits, and the corresponding effective sets of time past perigee are obtained.

Low signal power density limits the performance of space-surface bistatic synthetic aperture radar (SS-BiSAR) using Global Navigation Satellite System (GNSS) satellites as illuminators. To tackle this problem, in this letter, a novel bistatic SAR image enhancement technique based on repeat-pass coherent fusion is proposed. The works in this letter include three aspects. First, repeat-pass experiments are designed to ensure the best resolution. Second, a modified CLEAN technique is applied to remove the direct signal interference from the focused BiSAR images. Third, a coherence-processing method is proposed to implement coherence of each repeat-pass BiSAR image and then they are coherently fused to obtain a quality-improved BiSAR image. Twenty-two days of repeat-pass BiSAR experiments with Beidou-2/Compass-2 inclined geosynchronous orbit satellites as illuminators have been designed and conducted. The data were processed by the proposed method. The results show that the method can obtain better image quality compared with the traditional noncoherent fusion method and the single-day imaging result, which validates the proposed method and proves the huge potential in realizing local area monitoring with SS-BiSAR using GNSS satellites as illuminators.

The background ionosphere time variance within the extremely long integration time of Geosynchronous Synthetic Aperture Radar (GEO SAR) needs to be considered for GEO SAR focusing. Meanwhile, because of the curved trajectory and the very complex geometry relationship between satellite motion and earth rotation, the traditional analysis method of background ionosphere on LEO SAR imaging will no longer suitable in GEO SAR. In this paper, a GEO SAR signal model is proposed to describe the effects of total electron content (TEC) variance within the long integration time. Then, a GEO SAR signal two-dimensional spectrum under the effects of background ionosphere is derived for the first time. The GEO SAR defocusing phase errors are derived analytically. At last, the US Total Electron Content (US-TEC) data are used and the focusing results are presented, verifying the analysis.

One of the GEO SAR's characteristics of long integration time guarantees its fine resolution. But on the contrary, the ultra-long time boosts the multiple influences and then affects the focusing severely. In this paper, a validation experiment is presented to verify the feasibility of GEO SAR imaging under the condition of long integration time of around several hundred or even thousands of seconds. The experiment employs the Chinese Beidou IGSO navigation satellites as the illuminator of opportunity. The receiver is deployed on the top of a building and then a space-surface Bistatic SAR (SS-BISAR) configuration is constructed. A transponder consisting of two antennas and an amplifier is constructed for evaluating the resolution. The images of the transponder and the natural scene are focused well, which can validate the GEO SAR imaging feasibility though the linear track and constant speed assumptions fail.

Geosynchronous Synthetic Aperture Radar (GEO SAR) will be affected by ionosphere scintillation inevitably because it usually works at L band. In this paper, GEO SAR signal model in presence of ionospheric scintillation is proposed. The scintillation sampling model is employed to simulate ionospheric scintillation data. Several GEO SAR imaging simulations in different scintillation cases and a real ionospheric scintillation measurement in Zhuhai district of China are conducted to study the impacts of ionospheric scintillation on GEO SAR in azimuth. The results suggest that ionospheric scintillation will worsen the azimuth resolution, and rise azimuth peak sidelobe ratio (PSLR), and severely deteriorate azimuth integrated sidelobe ratio (ISLR).

Geosynchronous synthetic aperture radar (SAR) (GEO SAR) has the characteristic of long integration time; thus, the time-freezing model assumption of background ionosphere for traditional low Earth orbit (LEO) SAR no longer holds in GEO SAR. Furthermore, the background ionosphere variation within the integration time cannot be omitted either. In this letter, the variation of total electron content within integration time is analyzed and described in detail by using polynomial approximation, and a new GEO SAR signal model influenced by background ionosphere is also proposed. In view of this novel model, the analytical expression of image shift and defocusing phase error are derived in the first place. Then, a quantitative analysis for the image shift and image defocusing in the range and azimuth directions is conducted, and the performance bounds of time-varying parameters of background ionosphere effects on focusing are obtained. Finally, the U.S. Total Electron Content measured data are used to verify the theoretical results of background ionosphere effects on GEO SAR focusing.

This paper analyzes the multiangle imaging results for bistatic synthetic aperture radar (BSAR) based on global navigation satellite systems (GNSS-BSAR). Due to the shortcoming of GNSS-BSAR images, a multiangle observation and data processing strategy based on BeiDou-2 navigation satellites was put forward to improve the quality of images and the value of system application. Twenty-six BSAR experiments were conducted and analyzed in different configurations. Furthermore, a region-based fusion algorithm using region-of-interest (ROI) segmentation was proposed to generate a high-quality fusion image. Based on the fusion image, typical targets such as water area, vegetation area, and artificial targets were compared and interpreted among single/multiple-angle images. The results reveal that the multiangle imaging method was a good technique to enhance image information, which might extend the applications of GNSS-BSAR.

This paper presents the theory, algorithm, and results of a new bistatic interferometry synthetic aperture radar (InSAR) method. It employs the data acquired in an innovative bistatic configuration, which uses the orbital sensors as transmitters of opportunity and the stationary receivers on the ground, to generate a digital elevation model (DEM). In the bistatic spaceborne/stationary InSAR configuration, the interferometric phase only depends on the target-receiver range, which could not be obtained directly from the measured bistatic range. Therefore, the conventional transforming relationship between the interferometric phase and the topographic height is no longer practical. In order to solve the problem, we introduce a new conversion relationship between the interferometric phase and the topographic height, which is derived by the model of the ellipsoidal projection in the bistatic configuration. Meanwhile, the error analysis of the new conversion is carried out through a simulation. Both the simulated and measured data are used to test and verify the feasibility of the new bistatic InSAR method. In the spaceborne/stationary InSAR experiment, YaoGan-3 (an L-band spaceborne SAR system launched by China) was selected as the transmitter and two stationary receivers were mounted on the top of a tall building. The generated DEM of high quality shows that the presented method performs very well in the bistatic InSAR data process.

We present a theoretical analysis for InSAR height measurement in sand-covered arid areas based on two-layer model. The influence of radar wave's penetration on height estimation is mainly considered. If the returned signal from the subsurface is dominant, InSAR is potential to measure the subsurface height. Taking the refraction at the interface and the change of propagation velocity in the sand layer into account, a modified InSAR method is proposed in this paper. Simulations are performed to validate the proposed subsurface InSAR method.

Geosynchronous (GEO) SAR has been proposed as a means for obtaining observations of the Earth with finer temporal sampling than possible with a single satellite from a lower orbit. However, standard algorithms developed for low-Earth-orbit SAR imaging are inadequate for GEO, where the typical assumptions of quasi-linear trajectory and “stop-and-go” transmit/receive propagation break down because of the long integration time and the very long range between the satellite and the Earth. This paper proposes a curved trajectory model to overcome these limitations and considers the impact of the “stop-and-go” assumption. According to the proposed range model, an accurate 2-D analytical spectrum is deduced under the curved trajectory model based on a series reversion method, leading to an improved frequency domain imaging algorithm involving a high-order-phase coupling function and a range migration correction function. An adaptive azimuth compression function overcomes the space variance for large-scene focusing. Simulation results validate that the improved imaging algorithm performs well over the expected range of applicability for GEO SAR.

Synthetic aperture radar (SAR) simulation of 3-D forest canopies is a powerful tool for studying the interaction between radar and forest, for testing new applications, and for devising inversion algorithms of forest structures. SAR raw-signal generation is frequently used in point-target simulation but is rarely used in 3-D forest simulation. The existing simulators directly produce SAR images based on an impulse response function (IRF) without involving raw-signal generation and various nonideal factors. In this paper, a novel simulator to produce SAR images of 3-D forest canopies is proposed. It incorporates a SAR raw-signal generation process taking account of various nonideal factors such as trajectory deviation of radar platforms and complexity of natural environments, which is more faithful to realistic remote sensing systems. Furthermore, an approach to speed up the raw-signal generation is put forward based on the equivalent scattering model consisting of a few virtual scatterers with specially calculated positions and backscattering matrices. Thus, the raw signals received from the entire forest canopy can be equivalent to those from virtual scatterers in the case of tiny slant-range errors. The error sensitivity of equivalent conditions is analyzed, and the optimum selection of equivalent parameters is derived considering the compromise between precision and efficiency. The results of simulation and forest height inversion demonstrate the feasibility and potential utilities of the proposed simulator.

This paper proposes an extended nonlinear chirp scaling (CS) image formation algorithm for the bistatic synthetic aperture radar systems with the squinted transmitter and a fixed receiver. Since the transmitter with the squint mode was adopted in the system, two main problems, i.e., the spatial variance of the frequency-modulation rate and cubic phase terms, were introduced in the image formation algorithm. The former problem was solved by the linearity approximation of parameter$p$and deduced$q$(the second- and third-order coefficients of CS factors in range, which could be used to remove the spatial variation and high-order phase in the range direction) along the range domain while the latter one was compensated by a cubic analytical phase term in the frequency domain. A corresponding experimental hardware system and the bistatic experiments were also described in this paper. Both the simulation and experimental results validated the proposed algorithm.

In geosynchronous (GEO) synthetic aperture radar (SAR), the synthetic aperture time (SAT) is very long and the trajectory mode should be considered as curve. This paper mainly analyzes the impacts of target azimuth position displacement on GEO SAR image formation. Because GEO SAR imaging need be based on the curved trajectory, targets' azimuth position displacement on GEO SAR image formation can affect targets' defocusing. This point will be analyzed by comparing two-dimensional (2-D) spectrum in GEO SAR and low earth orbit (LEO) SAR and some simulations. In the imaging formulation, to focusing a large scene, we present an improved secondary range compression (SRC) algorithm. In this proposed SRC algorithm, the azimuth compression function can adapt itself by changing the azimuth displacement. Finally, a large scene's imaging under the long SAT is realized.

This paper proposes a highly accurate bistatic range migration algorithm (RMA) for space-surface bistatic synthetic aperture radar (BiSAR) systems with asymmetric configurations when the transmitter moves along a rectilinear trajectory and the receiver stays at a fixed location. The main work here includes three aspects. First, by introducing a 2-D change of frequency variables, an analytical 2-D spectrum formula without any approximation was derived at the first time. Second, with the linearity approximation of the derived phase, a highly accurate bistatic RMA was proposed and guaranteed that the algorithm could be applied to focus the echo from a large target scene. Furthermore, the nonlinear phase of the linearity approximation was analyzed to avoid the defocus effect in the final BiSAR image. Both simulation and indoor experimental results are presented to validate our analysis.

The paper firstly analyses the error caused by the linear trajectory model and the Fresnel approximation because of the long synthetic aperture time in Geosynchronous Synthetic Aperture Radar (GEO SAR), and then proposes an improved Secondary Range Compression (SRC) focusing algorithm to overcome the effect of linear trajectory model and the Fresnel approximation. The improved focusing algorithm adopts the curved trajectory model on basis of Norm operator to remove the error caused by the linear trajectory, derives and compensates the third order phase in two-dimensional frequency domain to overcome the large range migration. Finally, imaging results verify the improved focusing algorithm.

Yaw steering is a method of Doppler centroid compensation performed by attitude steering of the satellite platform. However, the conventional yaw steering method does not work well as the satellite orbit height becomes higher, such as with the geosynchronous Earth orbit synthetic aperture radar (GEO SAR) which has 36 000-km orbit height. This letter primarily analyzes the Doppler properties of the GEO SAR in the satellite local coordinate systems, and gives the calculation formulas of the Doppler centroid frequency and instantaneous Doppler bandwidth. Based on the vector analysis, a new calculation method is proposed for the 2-D attitude steering angles in GEO SAR. The new method, considering Earth's rotation and elliptical orbit effects, can accurately compensate the Doppler centroid to zero. Then, according to the 2-D attitude steering angles and the characteristics of GEO SAR, the 2-D phase scan method is put forward to carry out the highly accurate compensation of Doppler centroid, which can avoid the difficulty of rotating and stabilizing the GEO SAR system. The simulations validate the conclusion in this letter.

In Geosynchronous SAR (GEO SAR), because of the increase of orbit height, the signal propagation delay time reaches up to hundreds of milliseconds, and the synthetic aperture time also reaches up to hundreds of seconds which will result in a curved synthetic aperture trajectory, thus the “Stop-and-Go” assumption and conventional imaging methods of low earth orbit SAR (LEO SAR) will lose effect in GEO SAR. In addition, because the angular velocity of earth rotation is approximately equal to that of satellite rotation, the Doppler parameter and resolution analysis in LEO SAR cannot be directly used too in GEO SAR either. In this paper, firstly, the accurate slant range model in GEO SAR is created based on the consideration of the error of “Stop-and-Go” assumption, and then the improved imaging method is proposed to compensate for the error of “Stop-and-Go” assumption and the effect of the curved synthetic aperture trajectory. Finally, based on the generalized ambiguity function (GAF) and projection theory, the accurate Doppler gradient vector is analytically obtained based on the consideration of earth rotation in GEO SAR, and the accurate resolution calculation in arbitrary direction is also derived in detail. All the simulation results verify the correctness and effectiveness of the proposed imaging method and resolution analysis method.

In this paper, considering the relative motion between satellite and earth during signal propagation time, the accurate analysis method for propagation slant range is presented in geosynchronous SAR. Furthermore, the difference between accurate analysis method and `Stop-and-Go' assumption is analytically obtained. Meanwhile based on the derived accurate slant range model, it is found that the corresponding range migration correction and azimuth reference function must consider the high order term, and therefore the modified SPECAN algorithm is proposed. The simulation results verify the correctness of `Stop-and-Go' assumption error derivation and SPECAN algorithm modification.

The key problem of the imaging processing in geosynchronous earth orbit (GEO) SAR system is the space-variance of the Doppler parameters, which will result in the defocus of the SAR image with classical imaging algorithm. In this paper, a Modified Range Migration Algorithm (RMA) is proposed to overcome the space-variance of Doppler parameters by compensating the velocity change along the location of the target. Furthermore the simulation results fully verify the effectiveness of derived algorithm after velocity compensation.

In this paper, the effect of the polarization and the multipath on shadow inverse synthetic aperture radar (SISAR) imaging is analyzed respectively in forward scattering radar (FSR). The multi-polarization and the multipath imaging results of targets, based on SISAR, are discussed respectively. In addition, the target forward scattering (FS) RCS under multi-polarization conditions are also obtained using CST simulation software to research the effect of multi-polarization on moving target feature recognition in FSR.

This paper presents algorithms designed for a subclass of bistatic synthetic aperture radar (BSAR) called space-surface BSAR (SS-BSAR). Two SS-BSAR configurations are considered. The first one assumes a stationary, spaceborne transmitter and a moving airborne receiver. The second case is the generalized SS-BSAR configuration, where the transmitter is nonstationary. The transmitter and receiver have essentially different flight paths and velocities. For each configuration under investigation, the characteristics of the corresponding SS-BSAR received signal are examined first. Then, each proposed algorithm is derived analytically, and verified via simulation.