With the increasing number of electromagnetic devices, radio frequency interference (RFI) suppression has gradually become an essential problem in synthetic aperture radar (SAR) imaging. Faced with complex RFI environments such as time-varying and multitype mixing that may occur, traditional approaches often result in inadequate suppression and loss of valuable echoes. Moreover, the representation ability of manually extracted features is limited, struggling to maintain consistent performance in complex electromagnetic environments. To tackle these challenges, this article proposes a feature-enhanced low-rank and sparse decomposition network (FELS-Net), which separates RFI and useful echoes in the time-frequency domain (TFD). We introduce two learnable invertible nonlinear transforms to enhance the representation of RFI and SAR echoes, and unfold the RFI suppression scheme based on low-rank and sparse decomposition into a parameter-learnable network structure. The strong interpretability of model-driven architecture offers a potential stability guarantee for RFI suppression performance, while deep learning (DL) contributes to more effective feature characterization and more efficient and robust parameter schemes. Experimental results demonstrate that the proposed method outperforms comparative approaches in both time-frequency (TF) and image domains, exhibiting robust performance across diverse experimental conditions.

Range sidelobe is a common and widely concerned issue in synthetic aperture radar (SAR) imaging. The sidelobe of strong scatters could cover weak targets and reduce the quality of SAR images, increasing the difficulty in interpretation. Due to the principle of energy conservation, reducing the sidelobe will reduce the range resolution of SAR for the single waveform design. By taking full advantage of the degrees of freedom of the transmitter, transmitting multiple waveforms, and exploiting the information discrepancy between pulses, we can overcome this limitation. Complementary sequences are a typical example since the range sidelobe of the sequences can be theoretically summed up to zero. Due to the transmission of different waveforms between pulses, an amplitude-phase modulation will be introduced in the azimuth dimension of SAR echo, which, in turn, leads to the Doppler spectrum aliasing and unexpected energy spikes outside the main energy region of SAR point spread function (PSF). Seeking to suppress the range sidelobe and meanwhile mitigate the spikes caused by waveform agility in SAR, we first establish the expression of the echo under the agile transmitting mode. Then, we propose an SAR PSF shaping (SAR-PSFS) method to suppress the integrated sidelobe level (ISL) of the whole range sidelobe plane in the SAR image via complementary waveform optimization. The inexact alternating direction penalty method (IADPM) framework is adopted to solve the resulting nonconvex optimization problem. Simulation results show that the proposed method outperforms the nonlinear frequency-modulation (NLFM) signal with a 5.46-dB lower integrated sidelobe ratio (ISLR) under the same mainlobe width. Besides, the range sidelobe can be effectively canceled with a 3.26-dB lower peak sidelobe ratio (PSLR) and a 4.99-dB lower ISLR compared with the Hanning window while maintaining the range resolution with the spikes caused by waveform agility effectively mitigated.

Obtaining the clear contours of ship targets via Synthetic aperture radar (SAR) is extremely valuable for monitoring the sea. Now there are many deep learning imaging methods for ground scenes with good results, but they will face three main challenges when imaging ship targets: 1) The translational and rotational motions of ship targets during travel and due to waves respectively bring spatial invariant and variant errors that are tough to be estimated and compensated, resulting in defocused SAR imaging results; 2) The varying motion of ships demands high generalization ability of the imaging reconstruction network to adapt to the ship targets with sample-wise variant motion parameters; 3) Since ships are noncooperative targets, the accuracy of motion parameter estimation should be evaluated based on image quality, whereas the available SAR image quality assessment functions exhibit limited robustness. To address these issues, this article proposes a deep learning-based SAR imaging framework for ship targets via deep unfolding. Firstly, the motion model and characteristics of ship targets are analyzed, and the SAR imaging model for ship target with complex translational and rotational motion is established. Secondly, an imaging network with high generalization ability is proposed to adapt echoes for imaging under different motion parameters. On this basis, a SAR ship image quality assessment network is proposed to assess the imaging results of SAR ship targets with different focusing qualities. Then, the high-resolution imaging problem of ship targets is regarded as a motion parameter optimization problem, with the image quality assessment results as the objective function. Finally, this problem is optimized to search for the most accurate translational and rotational motion parameter variables of the ship target, achieving error compensation and imaging. The validity of the method is verified though the simulation of point targets and real SAR scenario data.

Synthetic aperture radar (SAR) usually uses linear frequency modulation (LFM) signal to image the target and clutter signal, which often causes the target to be overwhelmed by strong clutter. To solve this problem, we propose a waveform design method which can reduce the clutter and guarantee the SAR imaging performance. Firstly, the spectrum mask constraints of signal-to-clutter ratio (SCR) are obtained according to the scene prior information, and then the unimodular waveform of minimizing weighted integral sidelobe level (WISL) is designed. The final result is the SAR transmit waveform with spectral constraints. Simulation results show that the design can effectively achieve target enhancement and clutter suppression while maintaining SAR imaging performance.

Subaperture processing is a commonly used method for time-domain SAR echo simulation. However, due to the effect of geometry configuration for bistatic SAR, doppler aliasing will appear in the simulated echo data, and then there will be ambiguity in the imaging result. In this paper, by analyzing the causes of ambiguity and introducing the interpolation in range, a modified subaperture echo simulation method for bistatic SAR is proposed. At last, simulation results are illustrated to verify the effectiveness of the method.

With the increasing widespread use of synthetic aperture radar(SAR) systems in various environments, jamming have become a serious problem that they face. In many situations, these jamming affect SAR systems’ ability to gather information. Many anti-jamming methods are based on the classification of jamming types. In order to provide information about jamming types, it is necessary to design a classification method which can classify multiple types of jamming. Considering the complexity of jamming features, in this paper, multi-domain jamming feature analysis and a residual network with convolutional block attention module (CBAM) are proposed to classify SAR jamming. To train this jamming classification network and validate its effectiveness, a database containing different jamming simulations is generated. The simulation results show that this method has reliable classification performance for various types of jamming.

Localization and tracking are important components for ground-moving target indication (GMTI). The traditional range-Doppler (RD) localization model is always applied to locate stationary targets in bistatic synthetic aperture radar (BiSAR). However, for moving targets, this localization model is no longer applicable due to the strong coupling of position and velocity with Doppler frequency. To address the severe challenge, this article proposes a joint moving target localization and tracking method for BiSAR-GMTI via transmitter–receiver trajectories extraction and inversion. The key innovation is the derivation of closed-form localization results for moving targets, which is beneficial for the quantitative analysis of localization error. The proposed method is structured into three main parts. First, to accurately capture the range history trajectory information, a segmented fitting extraction and inversion framework is designed. Second, to estimate the moving target’s initial state and demonstrate localization observability, joint range localization equations are established where the closed-form localization result of the moving target is derived. Finally, by defining the appropriate state transition equation and observation equation, and incorporating particle filtering (PF), the position measurement errors of bistatic platforms are weakened, and accurate tracking of moving targets is realized. The proposed method only utilizes range information, mitigating localization errors caused by Doppler estimation inaccuracies effectively. Simulation experiments and real data both demonstrate the localization and tracking accuracy of moving targets.

In UWB (ultra-wideband) radar imaging, there are differences in the signal scattering characteristics of targets for different frequencies. The estimation of target scattering characteristics is an important research field in target recognition and structured imaging. To solve this problem, this paper combines the target scattering characteristic estimation and super-resolution imaging into a low-rank and sparse signal reconstruction problem. It proposes an ADMM (the alternating direction algorithm of multiplier) scattering characteristic estimation algorithm based on low-rank and sparse signal. First, the echo model of UWB signal is established according to GTD model. Then build the corresponding complete dictionary. Finally, the ADMM-LS algorithm is used to solve the multi-objective joint optimization problem. Numerical simulation experiments verify the algorithm to prove the effectiveness of the proposed method.

In the past, the parameters of SAR system were relatively fixed, which lead to the poor flexibility in the use of imaging resources. In this paper, the concept of cognitive radar is introduced into SAR, which can make the system adaptively optimize the resources required. We design a three-steps process for large-scale imaging, which consists of two reconnaissance process and one cognitive processing link. Finally, we design the parameters and resources of two reconnaissance process manually or by using genetic algorithm. Experimental results show that the parameters designed meet the demand of large-scale search and the genetic algorithm used can significantly reduce the resource cost in further reconnaissance and improve the flexibility of system.

Multi-core digital signal processor (DSP) is widely used in SAR real-time imaging system for its high-speed operation capacity and parallel working ability. The matching of the algorithm and the parallel architecture has great impact on imaging speed of SAR processing. This paper proposed an efficient imaging architecture based on multi-core DSP TMS320C6678. The system implements bistatic polar format algorithm (PFA), using two-dimensional Chirp-Z Transform and one-dimensional interpolation to realize two-dimensional resampling. The experimental results show that the proposed architecture can complete 1024×1024 points echo processing and output a 1024×1024 pixels image within 1.7 seconds.

As high-frame-rate synthetic aperture radar (SAR) has the ability to form continuous SAR images and dynamically monitor the ground areas of interest, it has attracted more and more attention nowadays. In practical applications, the enormous data in high-frame-rate SAR system to obtain the multiframe images brings big challenges to its transmission, storage, and processing. In order to solve this problem, there are many recent papers on formulating the high-frame-rate SAR imaging problem into a low-rank tensor recovery problem, and correspondingly, the sampling amount of the high-frame-rate SAR data can be largely reduced. However, existing algorithms to solve the low-rank tensor recovery problem in high-frame-rate SAR application still suffer from large computational cost. Under the above inspiration, this article proposes a deep neural network architecture for high-frame-rate SAR imaging, i.e., tensor alternating direction method of multiplier network (TADMM-Net), which is more computationally efficient in the imaging procedure. Specifically, we formulate the high-frame-rate SAR imaging processing into a low-tubal-rank tensor recovery problem. We solve the low-tubal-rank tensor recovery problem using a tensor alternating direction method of multiplier (ADMM) algorithm and then design a new deep neural network architecture by applying algorithm unfolding techniques to the underlying low-rank tensor recovery problem. The proposed TADMM-Net approach shifts the computational burden from the testing phase to the training phase, and the practical processing time can be extremely decreased compared with the existing algorithms for the low-rank tensor recovery problem in high-frame-rate SAR imaging applications. It also offers various advantages over the existing high-frame-rate SAR imaging algorithms, including higher performance in the case with low sampling amount, lower storage complexity, and no requirement for handcraft hyperparameters adjustment. The methodology was tested on high-frame-rate SAR data. These tests show that the proposed architecture outperforms other state-of-the-art methods in high-frame-rate SAR imaging applications.

To address scanning radar forward-looking sea imaging, this paper proposed a super-resolution method based on Weibull distribution. The proposed method in our work introduced the generalized Gaussian distribution and Weibull distribution to represent the prior distribution of the target and the sea clutter respectively, which are more suitable for actual sea imaging. And the corresponding objective function was derived under the MAP framework. In order to overcome the objective function's nonlinearity, this paper adopt Newton-Raphson iterative method to resolve it. Finally, through simulations, which indicates that the proposed method has superior imaging performance compared with other traditional methods for sea imaging.

The real aperture radar (RAR) system can present full-view observation capability, but the coarse azimuth resolution restricts its application. Therefore, various super-resolution deconvolution methods are widely used in the real aperture super-resolution imaging field. But conventional deconvolution approaches only rely on amplitude information of antenna pattern profile, it will behave worse when forward-looking imaging with high speed or squint imaging. This paper analyzes the influence of phase and constructs a corresponding amplitude-phase model to resolve this problem. Finally, the effectiveness of the proposed amplitude-phase convolution model for forward-looking imaging with high speed or squint imaging is verified by simulations.

With the continuous development of imaging radar technology, the bandwidth of transmission signals continues to increase. The geometrical theory diffraction (GTD) model can describe target scattering characteristics under wideband electromagnetic wave irradiation, which is significant for target detection and recognition. Existing target scattering center parameter estimation methods based on compressed sensing (CS) theory require sparse imaging scenes, which is difficult to meet in actual radar data. To solve this problem, this paper proposes a method for estimating high-value scattering centers parameters based on energy trajectory extraction. The estimation result of this method is accurate with fewer operations than CS-based method, and this method reduces the requirement for target sparsity. And use numerical simulation to prove the effectiveness of the method.

Synthetic Aperture Radar (SAR), an all-weather microwave remote sensing technology, is widely used in environmental monitoring, earth resource surveys and other fields, especially in the military field to achieve monitoring purposes. In the monitoring process, it is crucial to achieve real-time imaging of the target scene. Therefore, in this paper, an efficient SAR real-time imaging system is designed to achieve timely parallel processing of the scene when the radar platform scans the target scene, so as to reduce the consecutive frame cycles of real-time imaging. In which, the signal processing board structure consists of two FPGAs and six DSPs is used to quickly implement high precision SAR real-time imaging for airborne radar, so that the radar system can display the target image in time with the platform moving and check whether the current echo is valid. In addition, the architecture proposed in this paper can be extended in case the continuous frame period for real-time imaging is short.

Bistatic forward-looking synthetic aperture radar (BFSAR) is a kind of bistatic SAR system that can image forward-looking terrain in the flight direction of the receiver. Current literature and reports about BFSAR mainly concentrate on the stationary scene and ground-moving target imaging. Unlike stationary and ground-moving targets, the translational and rotational movements of ship targets usually lead to complicated range cell migration (RCM) and Doppler frequency migration (DFM). Moreover, the characteristics of RCM and DFM for different scattering points of the ship target are significantly different, i.e., the characteristics of the RCM and DFM are 2-D spatial variation, ultimately leading to severe defocusing of ship target in the SAR image. To solve these problems, a kind of hybrid SAR-ISAR imaging formation is proposed for BFSAR ship target imaging. First, to solve the problem of the Doppler ambiguity caused by the forward-looking mode of the receiver, an efficient ambiguity estimation method based on the minimum entropy criterion is presented. Then, keystone transform and range alignment processing can be applied to correct the spatial variant range walk and higher order RCM, respectively. Moreover, in order to obtain a high-resolution and well-focused image after translational compensation, a new method based on the fractional Fourier transform (FrFT) and the Wigner–Ville distribution (WVD) is proposed, where FrFT is applied to separate the multiple main scattering points in each range cell, and WVD is applied to obtain the high-resolution time–frequency distribution of each scattering point. Compared with the conventional ISAR range-Doppler (RD) algorithm and time–frequency estimation-based imaging methods, this method not only has no cross terms but also has high processing accuracy and better antinoise performance.

To realize high azimuth resolution for sea-surface targets, this paper proposes a superresolution imaging method that relies on the Weibull distribution. The proposed method introduces the generalized Gaussian distribution and Weibull distribution to represent the statistical distribution function of the target prior information and sea clutter, respectively. The corresponding objective function was derived under the maximum a posteriori (MAP) criterion. To address the nonlinearity of the objective function, this paper adopts the NewtonRaphson iterative method to resolve it. Simulations and experimental data assessment indicate that the proposed method has superior superresolution imaging performance compared with other traditional superresolution methods for sea-surface target imaging.

SAR image segmentation is a key step of SAR image interpretation, boosting target detection and recognition. Since similar targets may exist in complex and changeable scenes, under-segmentation and over-segmentation often occur in SAR image segmentation. To solve the above deficiencies, we propose a superpixel aggregation method based on multi-direction gray level co-occurrence matrix (GLCM) for SAR image segmentation. Firstly, a linear similarity judgment based on gray feature and spatial distance of pixels is introduced. In this stage, we expand the search range of clustering centers and add constraints to reduce the deviation, so as to alleviate over-segmentation. Then, for the spatial adjacent su-perpixels, we use multi-direction GLCM to measure texture similarity between them, merging homogeneous superpixel-s to solve under-segmentation. Experimental results based on satellite-borne SAR images from different scenes illustrate that the proposed method performs well with excellent pixel accuracy, effectively solving under-segmentation and over-segmentation.

The echo signal of slow-moving target is usually submerged in clutter signal. As a consequence, ground moving target (GMT) separation is challenging because of the decreasing of detection performance. To solve this problem, this paper proposes a moving target detection method with single-channel for bistatic forward-looking SAR (BFSAR) which mainly contains three steps. First, range cell migration (RCM) is corrected by Keystone transform. Next, Extended Nonlinear Chirp Signal (NLCS) algorithm is applied to equalize the spatial variant Doppler parameters and subpress clutter spectrum broadening. At last, according to the difference of Doppler FM rate between GMT and the stationary clutter, the Short Time Fourier Transform (STFT) is devoted to separate GMT from the stationary clutter. The effectiveness of the detection method proposed in this paper has been demonstrated and illustrated by numerical simulations.

Airborne radar high-squint looking imaging is an important research for remote sensing. The traditional Doppler beam sharpening based on fast Fourier transform (FFT) has good real-time performance but low cross-range resolution. Many super-resolution methods have been proposed to enhance the cross-range resolution for airborne radar. However, these methods generally adopt the batch processing mode with high computational complexity and high memory usage, which lead to poor real-time performance. This paper proposes an online super-resolution imaging approach for airborne scanning radar based on sliding window recursive least square (SWRLS) algorithm. The current scattering estimation can be derived recursively through downdating and updating. The proposed method effectively improves the cross-range resolution as well as the real-time performance and memory occupancy, which is beneficial to high-quint continuous realtime imaging for airborne radar. Simulation results are given to demonstrate the effectiveness of the proposed method.

Recently many deep neural networks have been utilized to learn and extract valuable features from synthetic aperture radar (SAR) images for SAR automatic target recognition (A-TR). However, in actual applications the types and amount of data that can be obtained are limited and difficult, which makes it hard to train the networks effectively. In this paper, we propose a multi-view deep learning framework combined with deformable convolution for SAR ATR. The scattering distribution characteristics and morphological characteristics of the target will be learned by the special structure of the deformable convolution, providing more sufficient information for subsequent fusion of features from the distinct views. Experimental results have shown the superiority of the proposed network based on the Moving and Stationary Target Acquisition and Recognition data set and the better recognition performance in the condition of a small number of raw SAR images.

Marine surveillance radar can monitor the marine environment in all-weather conditions, but the presence of sea clutter will seriously affect its target detection performance. In this paper, we proposes a sea clutter suppression method based on machine learning that contains two pairs of generative adversarial networks (GANs), in which one GAN is used to learn the mapping relationship of sea clutter suppression, and the other is used to ensure the performance of clutter suppression. Matching loss is proposed to preserve clutter suppression performance. Experimental results have shown the superior performance of the proposed method in improving the signal-to-clutter ratio (SCR) and the stability of clutter suppression.

Multistatic synthetic aperture radar (SAR) can adopt flexible topology structures to accomplish different missions. When we aim to coherently fuse multiple measurements of receivers, the topology structure of multi static SAR is the key to affect the imaging quality. In this paper, a topology design method based on wavenumber spectrum generation is proposed. The wavenumber spectrum distribution forms the dependency relationship between the imaging quality and topology structures. Based on the analysis of the kernel wavenumber spectrum distribution, the wavenumber spectrum generation is proposed to improve the spatial resolution. Using the generated wavenumber spectrum, the topology structure can be designed accurately. The proposed method effectively enhances the imaging resolution of multi static SAR at a low time cost. Simulation results verify the validity of the proposed method.

This paper presents a long-time integration method for passive radar detection of marine target with multi-stage motions using global navigation satellite system (GNSS) as illuminator. Owing to the complex motion, the range cell migration (RCM) and the Doppler parameters of the target echo pertaining the multiple motion stages are different. To solve the problems, in this method, first the range symmetry transform (RST) is applied to correct the RCM over the multiple stages, after which a one-dimensional azimuth signal can be directly extracted from the two-dimensional echo. Then, the conjugate integration processing (CIP) is performed to accumulate the target energy during each stage into Doppler centroid (DC) and Doppler frequency rate (DFR) domain. Finally, in order to settle the difference in Doppler parameters between multiple stages, the DC-DFR maps are projected and combined in high-dimensional Doppler parameter domain after Doppler frequency shift compensation, implementing the well integration of the echo over the entire long time. The effectiveness of the proposed integration method is demonstrated by simulations.

Synthetic aperture radar (SAR) is an important microwave detection for remote sensing and reconnaissance, and identifying the attributes of the targets from the SAR images which is known as SAR automatic target recognition (ATR), is an important tool in SAR applications. Many SAR ATR methods based on deep learning need to acquire a large amount of training data first, and the types and amount of data that can be obtained are limited and difficult in actual applications. Thus, we propose a deformable convolution neural network for SAR ATR, which not only extracts the scattering distribution characteristics of the target, but also extracts the morphological characteristics of the target through the special structure of the deformable convolution, providing more sufficient information for recognition. Experimental results demonstrate that the proposed network has an extremely high recognition rate in the moving and stationary target acquisition and recognition data, and it can maintain excellent recognition performance even when the amount of data gradually decreases.

In this paper, a weighted dense connected convolutional network(WDCCN) is proposed for SAR image super-resolution. In the network, to enhance feature propagation and the super-resolution performance, each layer will receive the output from all the previous layers in a different weight proportion. At last, the experimental results indicate that weighted dense connected convolutional network can realize SAR image super-resolution well.

Echoes of bistatic forward-looking synthetic aperture radar (BFSAR) disperse to multiple range cells and exist spatial frequency extension as well as Doppler spectrum extension (i.e., namely space-time extension). Furthermore, the characteristics of BFSAR clutter are strongly nonstationary and spatial variants. Because of the abovementioned issues, clutter and moving targets are fully overlapped in the initial 3-D space-time-range raw data domain, and the clutter cannot be suppressed effectively. To solve this problem, a BFSAR multipulse displaced phase center antenna (MP-DPCA) method is proposed in this article. First, keystone transform without Doppler ambiguity is applied to remove the coupling between the space-time and range domains. Hence, the overall complex 3-D processing in the space-time-range domain is reduced to independent 2-D processing in each space-time domain. Subsequently, a spatial-dechirp processing is applied in the space-time domain to eliminate the spatial frequency extension. Meanwhile, Doppler parameters of clutter point scatterers are equalized by nonlinear chirp scaling processing in the frequency and time domains. Accordingly, the Doppler spectrum extension of point scatterers can be eliminated by a uniform azimuth dechirp processing. After aforesaid three steps, the clutter and moving targets are separated in the space-time domain. Finally, based on the linear phase difference of clutter between the channels, a multipulse canceller can be designed to suppress the clutter. Compared with the existing space-time adaptive procession (STAP) and DPCA methods, this method not only overcomes the nonstationary problem in BFSAR but also conquers the strict application conditions of DPCA. Simulation results are given to verify the effectiveness of the proposed method.

This paper briefly introduces the basic scheme of a maritime moving target detection and localization method with multiple BeiDou satellites as transmitters. To detect the target and localize it, a receiver local-coordinates is constituted, whose axes are X, Y, and Doppler frequency rate (DFR), and the received echoes from multiple satellites are integrated into the local-coordinates. In this method, first symmetrical keystone transform (SKT) is applied to correct range cell migration (RCM) and set Doppler centroid (DC) to zero. Then, dechirp-FFT is applied to integrate each echo in fast-time and DFR domain. Finally, each DFR integration result is back projected to local-coordinates according to its bistatic range. From the final integration result, target can be detected and localized simultaneously. The effectiveness of the proposed method is demonstrated by simulations.

This paper proposes a clutter suppression method for the bistatic forward-looking SAR(BFSAR). The proposed method relies on a proper processing of the received echoes. First, keystone transform (KT) and the higher-order RCM correction are applied to correct the range cell migration (RCM). Then, azimuth nonlinear chirp scaling (NLCS) and azimuth dechirp are applied to suppress the spatial-variant Doppler spectrum extension. After the aforesaid two steps, the moving targets and clutter can be separated in the space-time domain, and the clutter signal with different pulses contains only one constant phase difference. Thus, the two-pulse clutter canceller can be designed to suppress the clutter. Finally, it is suggested that this proposed method can improve the signal to clutter ratio (SCR) and are beneficial for the subsequent signal processing, e.g. target detection. Simulation results are provided to validate the effectiveness of the clutter cancellation method.

With appropriate geometry configurations, bistatic synthetic aperture radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Thanks to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. In BFSAR, the compensation of the spatially variant motion errors is of great significance to get a well-focused image. In this paper, a motion compensation method based on keystone transform and modified autofocus back-projection is presented to deal with this problem. Keystone transform is applied to remove the spatial variation of range cell migration (RCM) and the first-order term of RCM errors simultaneously, prepares for the following modified autofocus back-projection, which can eliminate the high-order term of azimuth phase errors. Simulation results verify the validity and efficiency of the presented method.

Conventional synthetic aperture radar (SAR) reconstructs the illuminated scene via fast Fourier transform (FFT), which results in high sidelobe level, and poor cross-range resolution due to the finite synthetic aperture length. In this paper, we formulate a sparse reconstruction method for SAR imaging based on the covariance fitting criterion, which assumes that only a few strong scatters exist in the whole scene. The method is able to fully control over the sparsity level and reconstruct the scenario in an adaptive manner. Experimental results with real SAR data show the better performance of our method compared with the conventional methods in terms of resolution improvement and sidelobe suppression.

Due to the high altitude illuminator and the separation of the receivers and transmitter, Geosynchronous (GEO) spaceborne-airborne bistatic SAR (GEO BiSAR) is more flexible and accessible in remote sensing applications. However, by introducing a high speed airborne platform as receiver, azimuth spectrum aliasing occurs. In order to suppress the azimuth ambiguity without increasing the PRF of GEO SAR system, azimuth multichannel receiving technique is introduced to the airborne receiver in this paper. Firstly, the Doppler characteristics of GEO BiSAR are analyzed. Then, the multichannel transfer function for multichannel GEO BiSAR is derived and a modified multichannel reconstruction method is proposed to suppression the azimuth ambiguity. Finally, simulation results validate the effectiveness of the proposed method.

Geosynchronous (GEO) spaceborne-airborne multistatic SAR (GEO MulSAR) is flexible and accessible in remote sensing applications. Moreover, the information obtained by the multiple airborne receivers can be fused to enhance the spatial resolution. However, the fused spatial resolution significantly depends on the applied multistatic topology. In order to achieve the optimal fused spatial resolution by properly adjusting the imaging topology, a topology design method is proposed in this paper. Firstly, the spatial resolution model of GEO MulSAR is given and the dependance of the spatial resolution on the multistatic topology is analyzed. Then, a topology design method is proposed to obtain the best multistatic topology based on multiobjective optimization methods. Finally, the simulation results validate the effectiveness of the proposed method.

As for the bistatic forward-looking synthetic aperture radar (BiForSAR) imaging, frequency-domain imaging algorithms integrated with autofocus is advisable to get a well focused BiForSAR image in the presence of uncompensated motion errors. Nevertheless, a severe drawback of the autofocus algorithms is that they are only capable of removing one-dimensional azimuth phase errors. In range direction, there exists nonsystematic range cell migration (NsRCM) originated from motion errors and RCM correction procedure in frequency domain imaging algorithms as well. In this paper, the causes of different NsRCM in BiForSAR imaging are analyzed, and the corresponding relationship between different NsRCM is deduced. Based on the analyses, we propose an autofocus NsRCM correction scheme for BiForSAR imagery using frequency domain imaging algorithms, being capable of eliminating the range-dependent NsRCM.

Despeckle is a key post-processing step for Synthetic Aperture Radar (SAR) images [1], because speckles severely affect visual quality and intelligent interpretation. However the existing despeckle methods have a problem in texture preservation when the speckles are reduced. To solve this problem, image heterogeneity measurement is utilized to improvement the non-local means (NLM) despeckle method in this paper. First, an anisotropic window is constructed using coefficient of variation (CV), which is used for heterogeneity measurement. The anisotropic window are used to replace the isotropic window for the similarity measurement in NLM method. Furthermore, for weight function, an adaptive filter parameter based on heterogeneity measurement is designed to adjust the despeckling degree for different regions. The simulation results show that the proposed method outperforms than the existing algorithms.

Due to finite of information dimension of single bistatic synthetic aperture radar (SAR), to expand more information about ground objects we research multistatic SAR which can be decomposed into groups of bistatic SAR. It is known that scattering properties of the different viewing angles is different. In this paper, we focus on system of single transmitter and triple receivers and use polar format algorithm(PFA) to obtain images of ground objects for the triple receivers. A geometric distortion correction method is proposed due to elevation of ground objects. After the distortion correction, the three SAR images are registrated, then we put images into red, green, blue(RGB) channels respectively to realize fake color synthesis, and thus realize information fusion.

This paper presents an angular superresolution method based on maximum a posterior (MAP) criterion to improve the azimuthal resolution of forward-looking scanning radar in the background of sea clutter. Firstly, in consideration of the statistical property of sea clutter, the Rayleigh distribution is employed to express the likelihood function. And then, the generalized Gaussian constraint is used as prior information about targets for better property of noise suppression and positional accuracy. Finally, the iterative expression is derived to recover the scattering coefficient of original sea-surface targets. Compared to the conventional Bayesian approaches, The results of simulation experiment are given to verify the superior performance of proposed method.

Spatial resolution plays a key role in evaluating the quality of synthetic aperture radar (SAR) images. Due to its flexible geometry and configuration, bistatic SAR (BSAR) shows much more complex resolution characteristics than mono static SAR, such as coexistence of non-square and nonorthogonality. Traditional comprehensive metrics of spatial resolution ignored the non-square property. SAR-based template matching is widely used in various fields such as scene-matching-aided navigation and its performance is influenced by the spatial resolution of the SAR image. In this paper, the non-square property of the resolution of BSAR is analysed by the resolution ellipse. Then the template matching method and the metric of performance are presented. Finally, template matching performance of BSAR systems with resolutions of different non-square degree but same resolution cell area is studied and simulated.

Bistatic forward-looking synthetic aperture radar (BFSAR) is a promising technique for applications such as automatic navigation and landing in all weather conditions and in day and night. Conventional echo models are no longer valid for the BFSAR mounted on the emerging hypersonic vehicle (HSV) due to its high-speed large-acceleration motion characteristics. In this paper, by taking the nonlinear maneuvers of the HSV receiver during pulse propagation into account, a closed-form non-stop-and-go echo model named maneuvering-during-pulse-propagation echo model is proposed for HSV-BFSAR. The model errors are analysed theoretically. The simulation verifies the validity of the proposed model.

Geosynchronous (GEO) spaceborne-airborne multistatic synthetic aperture radar (GEO MulSAR) is more flexible and accessible in remote sensing applications because of the high-altitude illuminator and the separation of the receivers and transmitter. In addition, the information obtained by the multiple airborne receivers can be fused to enhance the spatial resolution. However, the fused spatial resolution severely depends on the applied multistatic topology. To achieve the optimal fused spatial resolution by properly adjusting the imaging topology, a topology design method is proposed in this letter. First, the spatial resolution model of GEO MulSAR is given, and the dependence of the spatial resolution on the multistatic topology is analyzed in detail. Then, a topology design method is proposed to obtain the best multistatic topology that simultaneously optimizes the resolution cell area and resolution disequilibrium factor. Finally, the simulation results validate the effectiveness of the proposed method, and some insights into designing the multistatic topology are given.

In synthetic aperture radar (SAR) imagery, images of ground moving targets (GMTs) are smeared, distorted, and shifted. Current GMT imaging methods are mostly based on range-Doppler algorithms, which have two main drawbacks: 1) coupling between range cell migration correction (RCMC) and Doppler parameter estimation and 2) cross terms degrade the performance of the nonlinear estimation methods. In this paper, an optimal 2-D spectrum matching method for SAR GMT imaging is proposed. The main innovation or advantage of this method is that the GMT imaging problem is transformed into a constrained optimization problem, and differential evolution is applied to guarantee a high-processing efficiency. As they are associated with the Doppler centroid variation compensation and range shift compensation processing, all GMT point scatterers can be well focused and well located. Compared with the current methods, the improvements exhibited by this method include three main benefits: 1) RCMC and Doppler parameter estimation can be simultaneously accomplished; 2) both along- and cross-track GMT velocities can be simultaneously estimated; and 3) this method can be applied to both monostatic and bistatic SARs. Numerical simulations and experimental data processing have verified the effectiveness and robustness of the proposed method.

In general, autofocus methods integrated with frequency-domain imaging algorithms are instrumental to obtain a well-focused bistatic forward-looking synthetic aperture radar (BFSAR) image in the presence of motion errors. Nevertheless, before applying autofocus methods to correct the azimuth phase errors, range cell migration (RCM) should be eliminated by the RCM correction (RCMC) procedure in frequency-domain imaging algorithms. With motion errors being taken into account, there always exists some residual nonsystematic RCM (NsRCM), which refers to the residual migration components after the RCMC procedure. For the conventional side-looking SAR, NsRCM is caused by motion errors. On the other hand, NsRCM of BFSAR is originated from motion errors before RCMC and the NsRCM amplified by the RCMC procedure. In this paper, we analyze the different types of NsRCM in BFSAR imaging and their relationship. Based on the analyses, we propose an autofocus NsRCM correction scheme for BFSAR imagery using frequency-domain imaging algorithms that can eliminate the range-dependent NsRCM. The proposed scheme consists of three steps. First, for the BFSAR data after range compression and RCMC, a division procedure is carried out in the azimuth direction. The subblocks with the highest signal-to-clutter ratio along the range direction are selected after the azimuth segmentation procedure. Second, for the selected subblocks, the total NsRCM is estimated based on the minimum-entropy criterion. Based on the estimation results, different parts of the NsRCM are obtained by solving an ordinary differential equation. Third, a two-step compensation of the NsRCM is executed to reach the spatially variant correction. Simulations and experimental results are provided to demonstrate that our proposed scheme is effective for BFSAR imaging.

This article presents an adaptive large-size moving-target imaging technique for bistatic forward-looking SAR (BFL-SAR). The main problems of this issue are that not only the echo characteristics (including range cell migration and Doppler parameters) are unknown, but also the spatial-variances of these characteristics are nonlinear for different point-scatterers. The proposed technique relies on a proper processing of the data aiming at, first, to correct the range walk by applying keystone transform over the whole received echo, and then, the relationships between the unknown high-order RCM, the nonlinear spatial-variances of the Doppler parameters, and the speed of the mover, are established. After that, using an adaptive NLCS technique, not only the unknown high-order RCM can be accurately corrected, but also the nonlinear spatial-variances of the Doppler parameters can be balanced. Numerical simulations show the effectiveness of this adaptive large-size moving-target imaging technique to be employed in BFL-SAR frameworks.

Breaking through the forward-looking limitation of monostatic synthetic aperture radar (SAR), bistatic forward-looking SAR (BFL-SAR) brings much potential in areas such as automatic navigation and landing in bad weather conditions. For many cases of BFL-SAR mounted on maneuvering-platform (MBFL-SAR), neither the flight path is straight nor the topography is flat. Conventional imaging algorithms designed for linear trajectory and flat topography fail to handle this situation. In this paper, a full-aperture efficient extended nonlinear chirp scaling algorithm with topography compensation is proposed for the data processing of MBFL-SAR. Simulation is presented to verify the validity of the proposed algorithm.

In bistatic forward-looking synthetic aperture radar (BFSAR), conventional autofocus algorithms cannot estimate the phase errors accurately when the range walk is compensated in the azimuthal time domain. This problem stems from the influence of the azimuth-variant Doppler coefficients after linear range cell migration correction in azimuthal time domain. In principle, nonlinear chirp scaling processing can be carried out to remove the azimuth variance. Nevertheless, Doppler azimuth variations of the Doppler parameters induced by the motion errors are not taken into consideration and serious defocus would emerge in the final image. To cope with such a problem, an estimation-evaluation-equalization scheme is proposed before conventional autofocus for BFSAR. Different from the conventional autofocus method, the azimuth-variant Doppler coefficients are additionally estimated and equalized before autofocus, and as a consequence, phase errors can be precisely estimated. The BFSAR data processing results demonstrate the validity of the proposed method on the improvement of autofocus in BFSAR.

In this paper, SAR MT imaging and velocity estimation method is proposed. The validity of this method is verified by numerical simulations. The main idea behind this method is to transform the PE problem to be a SOP problem. The advantages of this method include two main aspects: (i) This method can handle the MT imaging problem for different SAR modes, such as mono-static SAR, bistatic SAR, etc.; (ii) Both the along-track and cross-track velocities of theMT can be simultaneously estimated. In addition, since the focusing processing is conducted in 2D spectrum domain and the optimal criterion is the local minimum entropy, this method don't need to find a dominated point scatterer during the process.

With appropriate geometry configurations, bistatic synthetic aperture radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Owing to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. In BFSAR, the compensation of the spatially variant motion errors is of great significance to get a well-focused image. In this paper, first, the spatial-variance properties of motion errors are analyzed analytically and quantitatively. Different from the side-looking monostatic and bistatic SAR, 2-D space-variant motion errors should be taken into consideration in BFSAR. The 2-D spatial variance of the motion errors can be categorized into two parts, range-variant motion errors of the transmitter and azimuth-variant motion errors of the receiver. Moreover, these two parts are independent of each other. Based on this property analysis, second, a motion compensation (MoCo) approach with cubic-order processing is proposed to deal with the spatially variant motion errors in BFSAR. In the cubic-order processing, the first-order MoCo is performed to correct the spatially independent motion errors on the raw data. The second-order MoCo is accomplished on the non-range-cell-migration (RCM) data to deal with the range-variant errors. After the second-order MoCo, since the signal direction of the non-RCM data coincides with the variant direction of the uncompensated phase errors, the azimuth-variant motion errors and slow time signal are coupled together. To cope with such a problem, the slow time signal is transformed into the direction perpendicular to the azimuth by a novel procedure named azimuth-slow time decoupling. At this stage, the coupling between the azimuth-variant motion errors and slow time signal has been eliminated. Azimuth-variant motion errors can be corrected precisely. Simulation and experimental results verify the effectiveness of the proposed method.

In the imaging processing of translational variant bistatic forward-looking SAR (TV-BFSAR), range cell migration correction (RCMC) is an essential procedure. Based on the azimuth variance property of the range cell migration (RCM), the keystone transformation is used for RCMC in TV-BFSAR. Before the RCMC, the ambiguity correction of the Doppler frequency must be done. However, the motion error makes the ambiguity correction hard to be realized. An iterative RCMC based on Keystone transformation scheme is proposed in this paper to overcome the challenge motion error. By searching for the correct Doppler ambiguity number based on the minimum entropy, this scheme corrects the RCM in TV-BFSAR. Simulations validate the effectiveness of this method.

The angular superresolution is of great significance for scanning radar in forward-looking imaging. There are many techniques documented in literature to enhance the angular resolution, of which deconvolution method and power spectral density(PSD)methods are favored and attain many interests. In this paper, we focus on analyzing the advantages and challenges of PSD methods in comparison with the deconvolution method. Firstly, three typical PSD estimation approaches are introduced, followed with the comparison with deconvo-lution method that summarizes the advantages and challenges of PSD methods in theory. Simulations are provided in terms of coherence and number of snapshots, which presents the performance of different PSD methods and Lucy-Richardson deconvolution method, better demonstrating the advantages and challenges of PSD methods.

Ground moving target detection (GMTD) for squint synthetic aperture radar (SAR) is a more challenging task than that of the broadside SAR. The severe range and azimuth coupling induced by the squint mode combined with the unknown motion parameters makes the problem of indicating the moving targets more involved. In this paper, a novel ground moving target detection method for squint SAR is proposed. Firstly, an extended Keystone Transform (KT) is performed to remove the range cell migrations (RCMs) of both the stationary and moving targets. Secondly, the range compressed data undergoes two extended azimuth nonlinear chirp scaling (EANLCS) process separately with different sets of parameters based on the stationary scene. Then, after azimuth de-ramp processing, two images are obtained where all the targets are well focused except for the moving targets. Finally, by subtracting the amplitudes of the two images, the stationary targets are eliminated and the moving targets are detected. This algorithm is suitable for large squint angle cases and is computationally efficient. Simulation results verify the effectiveness of the algorithm.

The butterfly algorithm is introduced to achieve fast imaging of near space circular synthetic aperture radar (SAR) raw data with high resolution and large scene. The fast algorithm benefits from projecting the frequency domain signal after matched filtering to subimage. Quad tree structures constructed on both signal and image spaces are used to project signal recursively. Following the traversal of the quadtree, the image resolution is ameliorated gradually. The algorithm run on parallel processors and fast imaging is accomplished. Except for the ability of precise and quick imaging, large scene imaging is also achieved. The simulation results show the high quality image with good resolution.

Near-space Synthetic Aperture Radar (SAR) possesses useful features such as High-Resolution Wide-Swath (HRWS) and high revisiting frequency etc., which are difficult for the conventional SAR, e.g. spaceborne and airborne SAR, to simultaneously provide. Slow-speed SAR is introduced to solve the contradiction between high resolution and wide swath of Near-space SAR and a frequency domain algorithm based on Two-Step Processing Approach (TSPA) and Stolt interpolation is proposed. Simulations are provided to demonstrate the validity of the proposed algorithm.

This paper presents a two-dimensional (2-D) generalized omega-K algorithm for high-precision processing of the data of missile-borne Synthetic Aperture Radar (SAR) with constant acceleration. The almost adequate expression of the 2-D spectrum of the echo signal is obtained by the Method of Series Reversion (MSR). Then a 2-D generalized Stolt interpolation is carried out. The proposed algorithm can be applied to situations involving large dive and squint angle and high maneuvering speed. Simulations are presented to demonstrate the validity of the proposed algorithm.

With appropriate geometry configurations, bistatic synthetic aperture radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Thanks to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications, such as self-navigation and self-landing. In the mode of BFSAR with a stationary transmitter (ST-BFSAR), the two-dimensional spatial variation makes it difficult to use traditional data focusing algorithms. In this letter, an imaging algorithm based on keystone transform and nonlinear chirp scaling (NLCS) is proposed to deal with this problem. Keystone transform is used to remove the spatial variation of range cell migration. NLCS can eliminate the variation of azimuth reference function. Numerical simulations show that by combining first-order keystone transform and azimuth NLCS operation, the raw data of ST-BFSAR can be well imaged.

Near space slow speed SAR owns the ability of sustainable and large-scene imaging. However, the output speed of azimuth image is slow due to the slow speed motion, which is a disadvantage for some applications. In this paper, the terrain observation by progressive scans (TOPS) mode is applied in near space slow speed SAR, and the signal properties are analyzed.

Due to left/right Doppler ambiguity, and the small difference in Doppler frequencies of adjacent points in flight direction, conventional monostatic SAR can not be used for forward-looking imaging. Then bistatic SAR with transmitter and receiver mounted on different platforms is coming into the eyes of researchers. In this paper, the feasibility of forward-looking imaging using airborne bistatic SAR is studied. Firstly, the potential two-dimension resolution ability of bistatic SAR for forward-looking imaging is discussed from the aspect of iso-Doppler and iso-range line. Then the airborne bistatic SAR experiment using a side-looking airborne transmitter and a forward-looking airborne receiver is described, and the preliminary results are presented. To the authors' knowledge, this is the first bistatic forward-looking SAR experiment in the world that both transmitter and receiver are mounted on aircraft platforms.

Pulse repetition frequency (PRF) redundancy exists in near space slow speed SAR due to slow speed motion, and then the beam can point to different scene at the same position. Hence, the large scene imaging can be realized. However, the range migration and azimuth resolution is azimuth variant, which makes the imaging difficult. In this paper, imaging mode of near space slow speed SAR is presented, and the variable aperture BP imaging algorithm is proposed. Simulations are given to verify the effectiveness of the imaging scheme.

Simultaneously real-time monitoring for several distributed areas of interest is getting important in many applications. Such as security guarantee system during Olympic Games and brushfire monitoring for power grid. However, current remote sensing systems can hardly meet such steep demands. In this paper, we introduce a brand new system of Balloon SAR with Multi-spotlight illuminate mode. The new system is qualified with high resolution imaging ability for several flexible areas. Meanwhile, real time monitoring for long time duration is available here.

A novel ground moving target detection and imaging model called Near-Space Slow SAR (NSS-SAR) is introduced in this paper. It is not only effective for fast-moving targets but also slow-moving and micro-moving targets, which is hardly possible for conventional airborne and spaceborne SAR. Meanwhile, this model only needs mono-channel to distinguish Doppler signature of moving targets from the competing ground clutter returns via Doppler filtering. In addition, following analysis demonstrates that the NSS-SAR also has the potential to simultaneously achieve high-resolution and wide-swath (HRWS) imaging. Simulations given at the end of this paper verify the validity of the new NSS-SAR mono-channel moving target detection and imaging model.

Non-uniform FFT (NUFFT) algorithm is widely applied to Communication, Medical Imaging, Radio Astronomy and so on. NUFFT contributes to SAR imaging and Near-Space SAR also becomes popular recently years. It is difficult to get accurate SAR image in Near-Space because of the obvious motion error and Wide-Swath when the vehicle is slow, so we propose an approach which uses twice NUFFT in ω - k algorithm to solve the problem in this paper. The first NUFFT can solve non-uniform sampling along the track, the second NUFFT can replace the Stolt interpolation and the final range inverse FFT in ω - k algorithm to increase the computational efficiency without reducing the accuracy of the SAR image. The new method is especially suited for Near-Space SAR imaging when the speed of the vehicle is slow.

Focusing bistatic SAR data in frequency domain requires the two dimensional (2D) point target reference spectrum (PTRS). In this paper, a 2D PTRS is derived based on Lof feld's bistatic formula (LBF). The spectrum in this paper combines the influence of the Doppler centroids and frequency modulated rates of the transmitter and receiver on the total Doppler contributions. The essential of this paper is to model the Doppler contributions of the transmitter and the receiver using power series. It can be used in extreme bistatic (like hybrid spaceborne/airborne) and high squint (like bistatic forward-looking) cases. The accuracy of the PTRS is verified using numerical simulation of point target.

With appropriate geometry configurations, bistatic Synthetic Aperture Radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Thanks to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. In this paper, we present a vehicle-borne BFSAR experiment, including the imaging principle, system setup, echo property and data processing result. In the experiment, the transmitter is located stationarily besides the moving receiver. The transmit and receive antennas both point to the up forward-looking area to illuminate a point target. To the authors' knowledge, this is the first result of BFSAR with stationary transmitter in the world right now.

Bistatic synthetic aperture radar (BSAR) with one fixed station (OF-BSAR) can be used in wide area surveillance, interferometry and etc. This paper analyzed the spatial spectrum of OF-BSAR. Analytical expressions of the spatial spectrum was given. Using this result, we can determine the resolution performance of OF-BSAR.

With appropriate geometry configurations, bistatic Synthetic Aperture Radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. With such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. In this paper, based on the resolution calculation using gradient theory, we give a general rule to determine the optimal geometry configuration of different modes of BFSAR. The results can be used to design BFSAR flight campaign and measure the performance of a specific BFSAR system.

Because of the great speed difference between the transmitter and the receiver of the spaceborne/airborne bistatic SAR (SA-BiSAR), the bistatic angle changes with time, which makes the geometric structure of the SAR system no longer invariant, and the imaging result distort seriously and dissimilarly. In order to solve this problem the geometric model of the SA-BiSAR is established in this paper at first. Then based on the analysis to cause the image distortion, a correction method for bistatic SAR distortion named Inverse-Projection is brought up. The simulation results demonstrate that this method can work well for spaceborne/airborne configuration.

A complete vehicleborne Bistatic Synthetic Aperture Radar (SAR) imaging experiment is presented in this paper. Some new technologies were used to solve synchronization problems. An algorithm named the Glide Window Echo CFAR was used to realize time synchronization and collect the echo. High stable local oscillator was employed to achieve frequency and phase synchronization, and the PRF was adjusted adaptively in the Data Acquisition Device in the receive station. Finally, the Range-Doppler algorithm with range migration correction was using to image the certain scene successfully.