High resolution range profile (HRRP) plays a crucial role in radar target recognition. In real-world applications, variations in operational conditions during testing, such as changes in depression angles, result in unsatisfactory performance for HRRP target recognition methods. One way to alleviate this issue is to augment training data with samples that embody the testing domain style. Therefore, we propose a domain-adaptive HRRP generation approach based on a two-stage denoising diffusion probability model (DDPM). In the first stage, we leverage the category labels as conditioning factors, ensuring precise category control over the pre-generated contents. In the second stage, we harness style information from reference samples to steer the pre-generated content closer to the testing domain. By augmenting training data with the generated samples, the disparity between two domains is bridged. Results on the moving and stationary target acquisition and recognition (MSTAR) dataset show that the proposed method improves the recognition rate by 2.01% and 4.93% for the data of 15° and 17° depression angle, respectively.

The temporal action localization research aims to discover action instances from untrimmed videos, representing a fundamental step in the field of intelligent video understanding. With the advent of deep learning, backbone networks have been instrumental in providing representative spatiotemporal features, while the end-to-end learning paradigm has enabled the development of high-quality models through data-driven training. Both supervised and weakly supervised learning approaches have contributed to the rapid progress of temporal action localization, resulting in a multitude of methods and a large body of literature, making a comprehensive survey a pressing necessity. This paper presents a thorough analysis of existing action localization works, offering a well-organized taxonomy that highlights the strengths and weaknesses of each strategy. In the realm of supervised learning, in addition to the anchor mechanism, we introduce a novel classification mechanism to categorize and summarize existing works. Similarly, for weakly supervised learning, we extend the traditional pre-classification and post-classification mechanisms by providing a fresh perspective on enhancement strategies. Furthermore, we shed light on the bottleneck of confidence estimation, a critical yet overlooked aspect of current works. By conducting detailed analyses, this survey serves as a valuable resource for researchers, providing beneficial guidance to newcomers and inspiring seasoned researchers alike.

Super-resolution neural networks have recently achieved great progress in restoring high-quality remote sensing images at low zoom-in magnitude. However, these networks often struggle with challenges like shape distortion and blurring effects due to the severe absence of structure and texture details in large-factor remote sensing image super-resolution. Addressing these challenges, we propose a novel Two-Stage Spatial-Frequency Joint Learning Network (TSFNet). TSFNet innovatively merges insights from both spatial and frequency domains, enabling a progressive refinement of super-resolution results from coarse to fine. Specifically, different from existing frequency feature extraction approaches, we design a novel amplitude-guided-phase adaptive filter module to explicitly disentangle and sequentially recover both the global common image degradation and specific structural degradation in the frequency domain. Additionally, we introduce the cross-stage feature fusion design to enhance feature representation and selectively propagate useful information from stage one to stage two. Quantitative and qualitative experimental results demonstrate that our proposed method surpasses state-of-the-art techniques in large-factor remote sensing image super-resolution. Our code is available at https://github.com/likakakaka/TSFNet_RSISR.

Spaceborne multi-channel synthetic aperture radar (SAR) is an effective means to realize high-resolution and wide-swath imaging. However, for spaceborne multi-channel SAR imaging of maritime moving targets, the target motion will cause undesired channel imbalance, i.e., phase error, and further introduce the spurious targets in the image. To solve this problem, this paper proposes a novel spaceborne multi-channel SAR imaging algorithm for maritime moving targets, which consists of sequential coarse imaging and accurate imaging. The key strategies are to separate different moving targets by coarse imaging and to estimate the phase error based on the relationship between phase errors and amplitudes of spurious targets. First, the quantitative relationship between phase errors and amplitudes of spurious targets is established. Second, based on coarse imaging results, different maritime targets are effectively separated. Then, based on the measured amplitudes of spurious targets, a cost function, which represents the difference between the real target velocity and estimated target velocity, is constructed and minimized to separately estimate the velocities and phase errors of targets. Moreover, to further improve the accuracy of estimation and to suppress the undesired effects caused by target defocusing, clutter, and noise, an iterative strategy is adopted. Last, by auto-focusing, a well-focused SAR image is obtained. The GF-3 dual-channel real data experiment is conducted. The results indicate that the spurious targets are well suppressed, which validates the effectiveness of the proposed algorithm.

Remote sensing image super-resolution (RSISR) plays a vital role in enhancing spatial detials and improving the quality of satellite imagery. Recently, Transformer-based models have shown competitive performance in RSISR. To mitigate the quadratic computational complexity resulting from global self-attention, various methods constrain attention to a local window, enhancing its efficiency. Consequently, the receptive fields in a single attention layer are inadequate, leading to insufficient context modeling. Furthermore, while most transform-based approaches reuse shallow features through skip connections, relying solely on these connections treats shallow and deep features equally, impeding the model’s ability to characterize them. To address these issues, we propose a novel transformer architecture called Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network (SPIFFNet) for RSISR. Our proposed model effectively enhances context cognition and understanding of the entire image, facilitating efficient integration of features cross-stages. The model incorporates Cross-Spatial Pixel Integration Attention (CSPIA) to introduce contextual information into a local window, while Cross-Stage Feature Fusion Attention (CSFFA) adaptively fuses features from the previous stage to improve feature expression in line with the requirements of the current stage. We conducted comprehensive experiments on multiple benchmark datasets, demonstrating the superior performance of our proposed SPIFFNet in terms of both quantitative metrics and visual quality when compared to state-of-the-art methods. Our code is available at https://github.com/Dr-Lyt/SPIFFNet.

Recently, remote sensing image super-resolution (RSISR) has drawn considerable attention and made great breakthroughs based on convolutional neural networks (CNNs). Due to the scale and richness of texture and structural information frequently recurring inside the same remote sensing images (RSIs) but varying greatly with different RSIs, state-of-the-art CNN-based methods have begun to explore the multiscale global features in RSIs by using attention mechanisms. However, they are still insufficient to explore significant content attention clues in RSIs. In this article, we present a new hybrid attention-based U-shaped network (HAUNet) for RSISR to effectively explore the multiscale features and enhance the global feature representation by hybrid convolution-based attention. It contains two kinds of convolutional attention-based single-scale feature extraction modules (SEM) to explore the global spatial context information and abstract content information, and a cross-scale interaction module (CIM) as the skip connection between different scale feature outputs of encoders to bridge the semantic and resolution gaps between them. Considering the existence of equipment with poor hardware facilities, we further design a lighter HAUNet-S with about 596K parameters. Experimental attribution analysis method LAM results demonstrate that our HAUNet is a more efficient way to capture meaningful content information and quantitative results can show that our HAUNet can significantly improve the performance of RSISR on four remote sensing test datasets. Meanwhile, HAUNET-S also maintains competitive performance. Our code is available athttps://github.com/likakakaka/HAUNet_RSISR.

Intent perception is a novel task that aims to understand the intention of images, regular classification methods usually perform unsatisfactorily on intent perception due to the semantic ambiguity problem,i.e. the intra-class variety problem in which images of the same intent class may contain objects of different semantic categories and the inter-class confusion problem in which images of different intent classes may contain objects of similar semantic categories. To address this problem, this paper introduces prototype learning into the intent perception and proposes a unified framework named PIP-Net to reduce the influence of semantic ambiguity. Specifically, for each intent class, we first filter semantic ambiguity samples which are far away from the cluster center. Then we use features of the filtered samples to generate prototypes via clustering algorithm. Besides, we enhance the diversity between prototypes of different classes to better handle the inter-class confusion problem. To update the prototypes in the training process, we introduce a global matching algorithm to holistically match each feature with class prototypes, and use the momentum update strategy to stably update prototypes. Experimental results on the Intentonomy dataset demonstrate that our method can consistently outperform the traditional classification paradigm in multiple baseline models, and verify the effectiveness of our proposed prototype learning paradigm in addressing the intent perception problem. Our proposed PIP-Net achieves a new state-of-the-art performance on Intentonomy, including Macro F1 score of 31.57% and averaging F1 score of 41.85%. Code is available athttps://github.com/CodeMonsterPHD/PIP-Net.

Dual-channel synthetic aperture radar (SAR) is a widely used technology to achieve high-resolution and wide-swath imaging in current SAR system. Due to the fact of nonuniform sampling and spectrum ambiguity, the Doppler spectrum must be reconstructed before focusing. However, the traditional spectrum reconstruction algorithms require large onboard storage memory and computational resources and hence, make it hard to realize real-time processing. To solve the problem, this letter proposes a blocked spectrum reconstruction algorithm for onboard real-time imaging. Different from the traditional ones, the new method updates parameters block by block in azimuth, and hence, reducing the storage and computational resources at the cost of sacrificing the accuracy of the spectrum reconstruction to some extent. Fortunately, the accuracy loss is acceptable under properly set focusing depth, i.e., azimuth block width. The simulation and real data experiments verify the proposed approach.

The performance of wideband radar detectors can be further improved if the features of a target's high-resolution range profile (HRRP) are fully utilized. In this paper, a target adaptive double threshold detection method is proposed. First, the amplitude distribution parameters of the HRRP's range cells are extracted for data generation, and the detection probability is statistically calculated. The online gradient descent method is then used to adaptively adjust the second thresholds of the double threshold detector, thereby improving the detection performance with a given constant false alarm rate. The effectiveness of the proposed algorithm is verified using two sets of measured data. The proposed algorithm exhibits superior detection performance compared to traditional detection algorithms for range-spread targets.

Earth-based repeat-pass synthetic aperture radar (SAR) interferometry (InSAR) is a powerful tool to obtain the high-precision lunar terrain and deformation information, due to the short revisit period as well as the all-weather, long-time, and large-coverage characteristics of radar lunar observation. In this article, the baseline formation mechanism is investigated. Theoretical analyses show that the interferometric baselines mainly come from the lunar libration; however, the lunar libration may also result in a significant change of baselines and further lead to serious geometric decorrelation. To address this problem and ensure the Earth-based repeat-pass InSAR of the Moon, the precise relative motion model between the radar and the lunar target, considering lunar libration, is established, and the subradar point (SRP) wagging phenomenon is analyzed. Then, to evaluate the effects of SRP wagging on InSAR coherence, a geometric decorrelation model based on azimuth angle and incident angle is proposed. Based on that, the spatial–temporal baseline for Earth-based repeat-pass InSAR of the Moon is analyzed based on numerical simulation. Results show that the temporal baseline has obvious periodicity with a cycle of about 27 days. When the temporal baseline is an integral multiple of 27, the spatial baseline usually has the minimum value and the geometric decorrelation effect is relatively slight. Moreover, using the geometric decorrelation criteria, we examine the effects of radar frequency, position, and target location on the quantity of viable interferometric pairs, which has guiding significance for the selection of radar system parameters, radar position, and lunar target area.

A distributed configuration is a promising realization of tomographic synthetic aperture radar (TomoSAR) 3-D imaging. It is able to implement single-pass tomographic imaging in a very short time and, hence, outperforms the traditional time-consuming multipass TomoSAR. It also outperforms the traditional single-platform TomoSAR by achieving a higher resolution in elevation by forming a much larger spatial baseline. However, there has been little research reported on the distributed TomoSAR so far. In this article, we, for the first time, experimentally demonstrate the great potential of the single-pass distributed TomoSAR 3-D imaging. The main contributions are threefold. First, a new distributed TomoSAR 3-D imaging model is built, characterized by using both inner monostatic and bistatic spatial configurations. Second, a new multistatic synchronization scheme is developed for accurately correcting both multistatic time and phase synchronization errors. Finally, a P-band distributed unmanned-aerial-vehicle (UAV) TomoSAR prototype with four separate stations is built with an elaborately designed time-division waveform for full data acquisition. To the best of our knowledge, this is the first distributed TomoSAR system. We have also implemented the first single-pass TomoSAR 3-D imaging experiment and successfully achieved a meter-level 3-D image in Pinggu, Beijing, China.

The spaceborne stripmap range sweep synthetic aperture radar (SS-RSSAR) is a new operation mode for efficiently imaging the regions squinted with satellite orbit. By nonuniformly implementing azimuth sampling, the SS-RSSAR is able to avoid the problem of transmission blockage when the slant range varies seriously. However, this advantage is achieved at the cost of inducing time-varying nadir echo and makes the valid and the nadir echoes inseparable in the time domain. In this study, we propose to suppress the nadir echo of the SS-RSSAR via waveform diversity on transmission. The key mechanism is to always make the valid and the nadir echoes in the same pulse interval have different waveforms characterized by high autocorrelation but low cross correlation performances. In this way, the nadir echo can be effectively suppressed by performing matched filtering with respect to the valid echo. The main contributions of this study are twofold: the analyses for the lowest required waveform number and the strategy of achieving sufficient waveform patterns. The validity of the presented approaches is evaluated via the extended-target computer simulations.

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.

Applying a continuous varying pulse interval (CVPI) sequence is of significance for an extremely high-resolution (EHR) spaceborne sliding spotlight synthetic aperture radar (SAR) for complete data acquisition. An optimum CVPI sequence should be the one leading to the minimum movement of the echoes of interest in a pulse train. However, such an optimization can be hardly conducted using the traditional timing diagram. Aiming at solving this problem, this article introduces a new graphic tool, referred to as the strip layering diagram, to achieve an optimum CVPI sequence for an EHR spaceborne sliding spotlight SAR. The basic rationale is to explicitly employ a new parameter$\eta $to directly control the movement of the echoes of interest in a pulse train. The new strip layering diagram consists of two layered subdiagrams: one is the candidate subdiagram, consisting of the candidate strips presenting all the CVPI sequence candidates restricted by the data acquisition geometry; the other is the feasibility subdiagram, consisting of the feasibility strips marking all the feasible regions where the echoes of interest are not submerged by the transmitted pulses or contaminated by the nadir echoes. An optimum CVPI sequence can be accurately and efficiently achieved by sketching a line segment satisfying four conditions inside the strip layering diagram. The detailed comparisons between the new diagram and the traditional timing diagram are also provided, followed by computer simulations to validate the presented approaches.

The compressed sensing (CS)-based synthetic aperture radar (SAR) imaging methods have emerged as the standard approach to obtain super-resolution (SR) SAR images and achieve extraordinary performances. However, they face three challenges. First, this kind of method is mainly based on the point scattering model and not suitable for characterizing the line-segment-scattering and surface-scattering features of distributed targets. Second, the hyperparameters in these methods are hard to tune to optimal values. Third, due to a large amount of calculation, these methods are difficult to apply in practice. In this article, to solve these problems, we introduce the line-segment-scatterers (LSSs) and rectangular-plate-scatterers (RPSs) in SAR echo model to develop the SAR hybrid echo model and propose two SAR parametric SR image reconstruction methods based on solving a CS problem, where three penalties are utilized to exploit the sparsity of the point scatterers, LSSs, and RPSs, respectively. At the core of the first method is a direct solver called multicomponent alternating direction method of multipliers (MC-ADMM) solver that solves the CS problem quickly and iteratively based on closed derivative expressions. In contrast, the second method maps the MC-ADMM solver into a deep unfolded neural network, i.e., the parametric SR imaging network (PSRI-Net), which is faster, and the parameters can be automatically set to the optimum. Since all the parameters of the MC-ADMM solver are learned discriminatively through end-to-end training in PSRI-Net. Extensive simulation and practical experiments are carried out to demonstrate the effectiveness of the proposed methods.

The edges of a target provide essential geometric information and are extremely important for human visual perception and image recognition. However, due to the coherent superposition of received echoes, the continuous edges of targets are discretized in synthetic aperture radar (SAR) images, i.e., the edges become dispersed points, which seriously affects the extraction of visual and geometric information from SAR images. In this article, we focus on solving the problem of how to recover smooth linear edges (SLEs). By introducing multiangle observations, we propose an SAR parametric image reconstruction method (SPIRM) that establishes a parametric framework to recover SLEs from SAR echoes. At the core of the SPIRM is a novel physical characteristic parameter called the scattering-phase-mutation feature (SPMF), which reveals the most essential difference between the residual endpoints of a disappeared SLE and points. Numerical simulations and real-data experiments demonstrate the robustness and effectiveness of the proposed method.

Linear array multiple-input multiple-output synthetic aperture radar (LA-MIMO SAR) tomography can provide 3-D radar images without layover and geometric distortion effects. However, suffering from the channel and motion error, and large computation, the real LA-MIMO-SAR data is difficult to be well focused quickly. Therefore, the 3-D imaging results of real LA-MIMO-SAR data are rarely shown in existing literature. In this paper, real LA-MIMO-SAR data imaging processing method based on 3-D fast factorized Backprojection is proposed to well focus the echoes with channel and motion errors, and large size of data. Then, our experiments of LA-MIMO-SAR 3-D imaging are demonstrated. The LA-MIMO radar is installed to a special tower crane and works in downward-looking and forward-looking geometry. Some 3-D imaging results are demonstrated to verify the downward-looking and forward-looking LA-MIMO-SAR modes and the processing method.

The spaceborne stripmap range sweep synthetic aperture radar (SAR) (SS-RSSAR) is a new-concept SAR mode proposed to efficiently image regions of interest (ROI) squinted with respect to satellite orbit. The most distinct feature of the SS-RSSAR is the beam sweeping in the elevation for continuous squinted ROI illumination. A wide nonlinear chirp scaling algorithm (WNLCSA) has been proposed to directly implement image formation along the ROI spreading direction. However, the algorithm efficiency is limited due to its time-consuming time-domain interpolation. In this letter, a new interpolation free WNLCSA (IF-WNLCSA) is proposed for more efficient SS-RSSAR imaging by substituting the interpolation with a much faster chirp scaling approach. Different from the interpolation, the chirp scaling induces additional range migration and Doppler modulation, both of which, however, are neglected due to their weak influences on imaging. The derivation of the chirp scaling is presented in detail, followed by the analyses on the algorithm accuracy and efficiency. The presented approach is evaluated by both the point- and extended-target simulations.

Dual-channel synthetic aperture radar (SAR) system is promising in high-resolution wide-swath (HRWS) imaging. However, gain and phase unbalance in the dual-channel SAR system will severely degrade the performance. To make clear the channel error effect on the image of the reconstruction algorithm, a precise analysis of the channel error effect is proposed in this letter. Based on the channel error effect analysis, the amplitude and position of the ambiguity caused by the channel error can be calculated, which is significant for understanding the image quality degradation caused by channel unbalance. Finally, based on the Chinese first dual-channel spaceborne SAR system-GF-3, the validity of this letter is verified by the simulated and real data, respectively.

Penetrating radar systems are widely used to image the objects that are buried inside mediums (such as walls, ground, and so on). However, due to the phase error caused by the refraction of mediums, the object images obtained by directly applying the imaging methods that ignore the existence of the mediums are defocused, which affects the recognition of small objects. Conventional focus imaging algorithms typically obtain a focused image by iteratively searching for optimal compensation, which is very time-consuming and can result in overfocusing. To solve this problem, a near-field phase cross correlation (PCC) focusing imaging algorithm is proposed in this article. First, a free-space imaging model is established to replace the unknown half-space (air-to-medium) imaging model. Then, by analyzing the relationship between the free-space and the unknown half-space imaging models, the phase error and a reference depth in free-space imaging model are determined. The focused image can then be obtained by compensating for the phase error, which is directly estimated by the proposed PCC method. The permittivity and object depth can then be estimated based on the slope of the phase error and the reference depth. Furthermore, the proposed algorithm can be extended to the multi-layered model in a straightforward manner. Extensive simulations and experiments are presented to validate the proposed methods. The results show that the proposed PCC algorithm can effectively compensate the phase error and obtain high-quality images, and the permittivity and object depth can be accurately estimated in single-layer medium cases.

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.

Geosynchronous synthetic aperture radar (GEO SAR)-related technologies are being mature, and the first GEO SAR satellite is expected to launch in the next ten years. Under this circumstance, some equivalent experiments should be conducted at the current stage to validate some key characteristics or parameters, which could significantly increase the success possibility of the GEO SAR project. To validate the feasibility of GEO SAR imaging with long integration time, which is the most important and fundamental characteristic of GEO SAR, the first helicopter platform-based equivalent GEO SAR experiment with long integration time was performed in Qianxi County of China on May 22, 2019. The integration time of it is 80 s, which is carefully designed to maintain the consistence between itself and the integration time of the GEO SAR. Furthermore, the azimuth signal-to-noise ratio gain with long synthetic aperture time is analyzed. Moreover, the 2-D space-variant motion error introduced by the complex helicopter trajectory and the performances of different imaging algorithms are analyzed to choose the proper imaging algorithms; to overcome the flaws and unclarities of existing algorithms, some improvements are proposed to obtain the well-focused SAR image. What is more, the equivalence of this experiment is also analyzed detailedly to demonstrate the effectiveness of this experiment. At last, the imaging result with synthetic aperture time of 100 s and the comparison between itself and the optic photograph validate the success of this equivalent experiment and the feasibility of GEO SAR imaging with long integration time.

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.

To realize real-time imaging for the spaceborne synthetic aperture radar (SAR) using the chirp scaling (CS) algorithm, high generation rates of phase factors and heavy computation loads are unavoidable. For example, updating phase factors continuously in the 2-D time or frequency space is difficult for generating phase factors for real-time imaging. To solve this problem, a modified CS algorithm based on updating phase factors regionally is proposed. Moreover, conventional floating-point arithmetic is unaffordable for real-time imaging onboard. Therefore, fixed-point processing is adopted. In the proposed imaging algorithm, the phase factors are updated only at some specified times and frequencies, and fixed-point operation is adopted. Furthermore, based on the paired echo theory and the finite word length error mathematical model, the effect of the proposed imaging algorithm on the SAR image quality is studied. Moreover, based on the above analysis and the experiments of Chinese HJ-1C and GF-3 spaceborne SAR practical measured data, a hardware system including digital signal processor and field-programmable gate array boards is constructed.

In this paper, with respect to multichannel synthetic aperture radar (SAR), we first formulate the problems of Doppler ambiguities on the radial velocity (RV) estimation of a ground moving target in the range-compressed domain, the range-Doppler domain, and the image domain, respectively. It is revealed that in these problems, the cascaded time–space Doppler ambiguity (CTSDA) may arise; that is, the time domain Doppler ambiguity in each channel arises first and then the spatial domain Doppler ambiguity among multichannels arises second. Accordingly, the multichannel SAR systems with different parameters are investigated in three cases with different Doppler ambiguity properties. Then, a multifrequency SAR is proposed for the RV estimation by solving the ambiguity problem based on the Chinese remainder theorem (CRT). In the first two cases, the ambiguity problem can be solved by the existing closed-form robust CRT. In the third case, it is found that the problem is different from the conventional CRT problem and we call it a double remaindering problem in this paper. We then propose a sufficient condition under which the double remaindering problem, i.e., the CTSDA, can also be solved by the closed-form robust CRT. When the sufficient condition is not satisfied, a searching-based method is proposed. Finally, some results of numerical experiments are provided to demonstrate the effectiveness of the proposed methods.

The interferometric phases in highly sloped terrain have the characteristics of large fringe density, narrow width, low correlation, and under-sampling. The local fringe frequ- ency (LFF) is a criterion to evaluate the trend and magnitude of the local terrain gradient and can be employed to improve the quality of interferograms. The results of the traditional LFF estimation method can be affected by phase noise, and sometimes the phase unwrapping (PU) operation is also required for some local regions. When it comes to highly sloped terrain, the phenomenon of phase under-sampling may cause incorrectness in the absolute interferometric phase during the operation of PU and may then influence the accuracy of the whole estimation. In order to solve this problem, this letter proposes an extended maximum-likelihood method for LFF estimation based on the multifrequency interferometric synthetic aperture radar (InSAR) data. Through the differences in the LFF between the different frequency InSAR data, the estimation quality map is introduced to modify the large error in certain regions by local 2-D fitting and thus achieves a accurate estimation of LFF in highly sloped terrain. Finally, the estimated results of LFF are used to guide the process of phase filtering. Simulated data and real airborne dual-frequency InSAR data are both employed to validate this proposed method.

Phase autofocus is a key step in translational motion compensation for inverse synthetic aperture radar. From the eigenvalue decomposition (EVD) of the covariance matrix generated by the aligned range-compressed signal, eigenvectors can be obtained for effective phase autofocus. However, as the number of pulse samples is increased to improve the cross-range resolution, the high computational complexity of the EVD may become burdensome. To address this problem, we propose a novel EVD-based method in this letter. When the number of range units is larger than the number of pulse samples, the conventional method is used. Otherwise, the transpose of the envelope-aligned data matrix is used to generate a lower dimensional covariance matrix and to perform successive autofocus processing. Since many real targets exist in limited range units, a one- or two-order-higher computational efficiency can be obtained in some typical scenarios with the proposed method, compared with existing EVD-based approaches. Furthermore, the equivalence between the above two methods has been proven in this letter. Finally, the results for real measured data are provided to demonstrate the effectiveness of the proposed method.

In many application areas ranging from bioinformatics to imaging, we are faced with the following question: can we recover a sparse vector xo∈ ℝNfrom its undersampled set of noisy observations y ∈ ℝn, y = Axo+w. The last decade has witnessed a surge of algorithms and theoretical results to address this question. One of the most popular schemes is the ℓp-regularized least squares given by the following formulation:x̂(y, p) ∈ arg minx(1/2)∥y - Ax∥22+ γ∥x∥pp, where p ∈ [0, 1]. Among these optimization problems, the case p = 1, also known as LASSO, is the best accepted in practice, for the following two reasons. First, thanks to the extensive studies performed in the fields of high-dimensional statistics and compressed sensing, we have a clear picture of LASSO's performance. Second, it is convex and efficient algorithms exist for finding its global minima. Unfortunately, neither of the above two properties hold for 0 ≤ pothan x̂(γ, 1). Second, if we employ iterative methods that aim to converge to a local minima of arg minx(1/2)∥y - Ax∥22+ γ∥x∥pp, then under good initialization, these algorithms converge to a solution that is still closer to xothan x̂(γ, 1). In spite of the existence of plenty of empirical results that support these folklore theorems, the theoretical progress to establish them has been very limited. This paper aims to study the above-mentioned folklore theorems and establish their scope of validity. Starting with approximate message passing (AMP) algorithm as a heuristic method for solving ℓp-regularized least squares, we study the following questions. First, what is the impact of initialization on the performance of the algorithm? Second, when does the algorithm recover the sparse signal xounder a “good” initialization? Third, when does the algorithm converge to the sparse signal regardless of the initialization? Studying these questions will not only shed light on the second folklore theorem, but also lead us to the answer the first one, i.e., the performance of the global optima x̂(γ, p). For that purpose, we employ the replica analysis1to show the connection between the solution of AMP and x̂(γ, p) in the asymptotic settings. This enables us to compare the accuracy of x̂(γ, p) and x̂(γ, 1). In particular, we will present an accurate characterization of the phase transition and noise sensitivity of ℓp-regularized least squares for every 0 ≤ pp-regularized least squares (if γ is tuned optimally) exhibits the same phase transition for every 0 ≤ pp-regularized least squares with different values of p. For instance, we will show that for very small and very large measurement noises, p = 0 and p = 1 outperform the other values of p, respectively.

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.

A new Doppler ambiguity resolver (DAR) is proposed for ground moving targets of synthetic aperture radar (SAR) in the image domain. Based on the range-Doppler imaging of a static scene, the moving target's response is analyzed in the image domain, and the target's response slope is jointly determined by three motion parameters, i.e., azimuth velocity, ambiguous range velocity, and Doppler ambiguity number. A new DAR utilizing these three parameters is then proposed via the following steps. First, the moving target is detected after ground clutter cancelation between dual-channel images. Second, the ambiguous range velocity is estimated, and the road and its slope are extracted from the SAR image to establish the relationship between the 2-D velocities. Third, the Doppler ambiguity number is determined based on the response slope, the road slope, and the estimated ambiguous range velocity. Compared with the existing DARs in the 2-D time domain, the proposed algorithm works better in the most common signal-to-clutter-noise ratio scenarios. Finally, the results of the numerical experiments are provided to demonstrate the effectiveness of the proposed method.

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).

Elliptical orbits are widely used in the system design of geosynchronous synthetics aperture radar (GEO SAR) to improve the temporal resolution. The orbit height variation in the elliptical orbit can cause the variation of nadir interference and beam illumination region. This shortens the available time of a designed pulse repetition interval (PRI) for a specific beam pointing, and multiple PRIs have to be used at different orbital positions. To lengthen the available time, a continuous PRI variation method is introduced in this paper. By varying the PRI with a constant time interval, the acquisition window and transmit interference changes with the variant beam illumination region, which loosen the constraint of the transmit interference. A design example of the proposed method is provided with significantly lengthened available time.

In this paper, a creative hybrid adaptive filtering algorithm is designed to combine a modified mode filter with an adaptive median filter for the suppression of interferometric phase noise which is a quality insurance of the following phase unwrapping operation. This combination provides the hybrid filtering algorithm with remarkable flexibility to terrain. At first, this paper presents a modified mode filter with an adaptive shortest sub-interval estimator. Then, it uses the mode filtering result as the variable local phase center of the median filter and improves it with an adaptive filtering window varying with the residue density and correlation coefficient. Finally, a hybrid adaptive filtering algorithm is proposed for the reduction of interferometric phase noise. The studies utilize the simulated Peak data and the real Ayers Rock data as data support. Through analyzing the phase residues and the PSD values of the filtering results, we finally prove the validity of this method.

The magnitude error and phase error (MEPE) in the transfer function of a stepped-frequency synthetic aperture radar (SAR) system results in a periodic MEPE in the synthesized wideband waveform, which induces the grating lobes in the high-resolution range profile. In this letter, a novel grating lobe suppression method based on the SAR image is proposed. In the paired-echo theory, a single sinusoidal term of the periodic MEPE in the frequency domain induces a pair of grating lobes in the time domain. Based on the magnitudes and phases of a strong scatterer and its grating lobes in the SAR image, the sinusoidal terms in the periodic MEPE can be estimated using the proposed method. By compensating for the estimated sinusoidal terms in the spectrum reconstruction, the corresponding grating lobes can be suppressed to the background level of the SAR image. The validity of the proposed method has been demonstrated using computer simulations and experiments based on real data.

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.

The challenge in the imaging of high-squint-mode synthetic aperture radar (SAR) mounted on maneuvering platforms is the azimuth dependence of both the range migration and the azimuth focusing parameters (the azimuth frequency-modulation rate and higher order coefficients), which are caused by range walk correction and acceleration. In order to accommodate the dependence, a modified subaperture imaging algorithm is proposed. Based on the fact that the azimuth times corresponding to the same Doppler frequency are different for targets in the same range gate, after making blocks in the azimuth frequency domain, the azimuth-dependent range cell migration correction is performed in the azimuth time domain for each block. Considering that the regions of support in the azimuth frequency domain are different for targets in the same range gate, the equalization of the azimuth focusing parameters is achieved by a new azimuth nonlinear chirp scaling method in the azimuth frequency domain. In order to verify the effectiveness of the proposed algorithm, the simulation of a point target array is presented. Furthermore, the real SAR data with a squint angle of 70° are processed, and high-quality images with a resolution of 1 m are provided.

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.

For satellites in geosynchronous orbit (GEO) using synthetic aperture radar (SAR), the direction of the satellite velocity vector in the Earth-centered, Earth-fixed (ECEF) coordinate system varies widely in one orbital period. When the velocity vector is approximately parallel to the range direction at equatorial latitudes, the 2-D sidelobes of the generalized ambiguity function (GAF) on the ground are non-orthogonal, which results in a significant deterioration in the ground resolution. To improve the ground resolution, this study investigates an optimal resolution steering (ORS) method for GEO SAR. The ORS method differs from the total zero Doppler steering (TZDS) method, which attempts to minimize the Doppler centroid. In the ORS method, a beam pointing direction is derived based on the constraints of optimal ground resolution. Then, a 2-D roll-pitch strategy is derived to obtain small steering angles, and the beam is steered to the optimal beam pointing direction. Finally, the ORS method is verified with simulations.

Unknown Noise-MUSIC (UN-MUSIC) is a promising method of direction of arrival (DOA) estimation in unknown spatially correlated noise using sparse arrays composed of two widely separated sub-arrays. The conventional UN-MUSIC estimator only utilizes information from one calibrated sub-array. If two sub-arrays are calibrated, a joint estimator that equally weights two sub-arrays’ conventional estimators has been found in literature. But no theoretical study has been reported. To compare and improve performance of different UN-MUSIC estimators, this paper proposes a unified framework of sub-array weighting to investigate the UN-MUSIC estimators, including the conventional one and the joint ones. The closed-form expression of the sub-array weighting estimator’s variance is derived which has not been done before. With the asymptotic results, different weighting strategies are compared and optimal weighting strategy to minimize estimation variance is proposed. Numerical simulations demonstrate the theoretical analysis and the validity of optimal weighting estimator.

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.

Airborne synthetic aperture radar (SAR) raw data are affected by motion errors due to atmospheric turbulence and aircraft properties, and this makes motion compensation (MOCO) an essential step. In order to decrease the influence of variable motion information, a kind of MOCO approach called azimuth-subaperture processing is widely used, which causes the azimuth-signal aliasing in squint SAR mode. Usually, this aliasing can be ignored, since its effect on final results is not worth mentioning. However, when the squint angle grows, it becomes serious and drops the quality of final images severely. To address this problem, an improved MOCO approach is proposed in this paper. Its main idea is to calculate the extended number of subapertures and eliminate the azimuth aliasing by extending the subaperture length. After theoretic analyses, simulations and experimental results are provided to demonstrate our proposed approach.

Aiming at the structural feature and polarimetric property preservation in speckle reduction of polarimetric synthetic aperture radar images, an improved speckle reduction algorithm based on structural judgment and hybrid four-component polarimetric decomposition (HPD) is developed. To protect the structural feature, a new structural judgment based on edge masks and pixels' sort is used for dividing pixels into three structural categories: bright, dark, and common targets. To protect the polarimetric property, a new scattering classification with HPD is employed to divide further the dark and common targets into four scattering classes. HPD inherits the physical significance of Pauli and Yamaguchi decomposition with its advantage of less time consumption. After that, without bright targets, a dark or a common pixel centered in a sliding window is filtered. To achieve better filtering results, pixels in its same and three neighboring scattering clusters from the same structural category are selected to filter. The experimental results of the UAVSAR and RADARSAT-2 data illuminate the validity of our proposed method.

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.

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.

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.

The phase gradient autofocus (PGA) algorithm is widely used for synthetic aperture radar (SAR) autofocusing. In practice, polarizations of electromagnetic waves result in phase error estimates with different precisions obtained by PGA, yielding distinctly focused images for different polarization channels after compensating the phase errors. In this paper, a new autofocusing technique for the polarimetric application is proposed. The algorithm employs the redundancy of phase error information among different polarization channels. The phase error estimate obtained by different polarization channels which optimizes the image quality with the highest image contrast is treated as the final estimation result. In comparison with the standard PGA, the performance of the proposed approach is demonstrated using real data from airborne SAR.

The clutter suppression of space-time adaptive processing (STAP) will degrade severely in forward-looking airborne radar if the range-dependence of clutter spectrum is not compensated correctly. In this paper, a new method for mitigating the range-dependence of the clutter spectrum based on the geometry-aided subspace projection is proposed. In the proposed method, the clutter covariance matrix of the range under test is firstly constructed based on the prior knowledge of antenna array configuration, and then the clutter subspace projection matrix is obtained by decomposing the corresponding covariance matrix. In the following, the mitigation of the range-dependence is accomplished by applying this clutter subspace projection matrix to transform the secondary range samples to obtain an approximate clutter subspace with the range under test. The simulation results show that the proposed method can mitigate the range-dependence significantly.

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.

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.

Doppler centroid is a key focusing parameter of Doppler beam sharpening (DBS) imaging. According to the locked relationship between Doppler centroid error and image entropy, a minimum-entropy Doppler estimator (MEDE) is proposed. The algorithm breaks the conventional algorithms' assumption that the shape of echo signal's power spectrum is symmetrical, and the proposed method can circumvent the problem of low estimation precision using conventional algorithms to process the echo signal from high-scene contrast. The new algorithm based on entropy minimization has high estimation precision in the case of low- and high-scene contrast. MEDE has been successfully applied to a DBS real-time imaging processor of airborne radar and verified by flight experiments.

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.

This paper presents a method to focusing the general bistatic synthetic aperture radar (BiSAR) data. First, using the extended Taylor series, the general BiSAR data are transformed into the azimuth-invariant BiSAR one. Second, the monostatic frequency scaling algorithm is extended to the bistatic one. The numerical experiment indicates that our methodology is valid and the proposed algorithm can process the data of the general BiSAR.

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.

Interference fringes will be generated in SAR image when distributed targets' echo signal is simulated because of the uniform model of targets. The fringes can be suppressed by adding random height in space or random phase on backscatter coefficient of the distributed targets. However, the former method will introduce location error and the latter method will increase speckle noise twice in simulated image. In this paper, we develop a high accuracy method for fringes suppression based on targets' position randomizing and RCS re-sampling via bilinear interpolation. The validation of method is successfully proved by simulation results and the performance of the method is discussed on accuracy and computational complexity at the end of this paper.

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.

In civilian communication systems, the signature sequence of the desired signal in training phase is known to the receiver. In this letter, using the mutual information, we bridge the probability density function and minimum mean-square error (MMSE) between the observed data and training sequence of the desired signal, and then employ the MMSE to construct a minimum description length (MDL) criterion for accurate source enumeration. Numerical results demonstrate that the proposed method is superior to existing MDL methods in terms of detection performance particularly for small number of snapshots and/or source angular separation.