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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This paper proposes an autofocus method based on minimum-entropy criterion for synthetic aperture radar imaging. Through minimizing the entropy, an iterative method is derived to obtain the phase error, which corrupts the image and increases the image entropy. In addition, dominant-scatter areas are selected as the input of the phase-error estimation. Note that there is not any model assumption for the phase error and the dominant-scatter area isn't required to only contain one point scatter. The phase estimation accuracy and efficiency are also analyzed in this paper. Finally, the simulation and experimental results are used to validate the feasibility and effectiveness of the proposed method.

This paper proposes a method of focusing the bistatic synthetic aperture radar (SAR) (BiSAR) data in spaceborne/stationary configuration. The key problem for imaging is the space variance of Doppler phase. The stationary platform induces additional and different range offsets to the range migration of targets. It causes targets with the same Doppler history, which are determined only by the moving platform, to shift into different bistatic range cells in the echo data. Therefore, the processing is not the same as monostatic SAR imaging which can be fast performed by the uniform matched-filter function in the frequency domain. In this paper, a modified nonlinear chirp scaling (NLCS) algorithm based on series reversion is formulated, which could achieve different range cell migration correction and the equalization of effective range and azimuth frequency modulation rates. The proposed algorithm is validated by simulated and real BiSAR data. In the spaceborne/stationary BiSAR experiment, the YaoGan-1 (an L-band spaceborne SAR system launched by China) is selected as the transmitter, and the stationary receiver is mounted on top of a tall building. The results show that modified NLCS algorithm can effectively focus BiSAR data with serious space variance in spaceborne/stationary configuration.

This paper mainly focuses on synthesizing high resolution profile for stepped-frequency radar signal. A novel method based on correlation coefficient is presented. Compared to traditional methods, the limitation of the frequency step size is relaxed. It means that the same range resolution can be achieved with less pulse number, thus reducing the complexity of the system design. Finally, the simulation data is used to demonstrate the performance of this proposed method.

The purpose of this paper is to solve the imaging problems of one-stationary bistatic synthetic aperture radar (BiSAR) squint mode. Through the analysis of the target's two-dimensional spectrum, an improved chirp scaling algorithm (CSA) is got. It can deal with the space variability of this kind of BiSAR and the simulation is done to verify the validity and feasibility of this algorithm.