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Radar detection of signals containing useful information about surveillance objects is a complex and multifunctional process that combines the solution of various problems, one of which is the detection of moving targets against the background of passive interference. Its solution is based on a fundamental idea based on the application of the Doppler effect, according to which, methods of selecting moving targets were developed, which consist in changing the frequency of the signal reflected from the moving object. Traditional sweep-to-sweep subtraction algorithm of passive interference of MTI systems, are effective when the interference is stationary, but have disadvantages caused by interference with a complex frequency spectrum. As a result, to improve the efficiency of radar detection systems, adaptive algorithms were proposed and investigated, and their advantages were shown. However, the need for further processing and improvement of existing systems and methods remains relevant and is caused by many aspects, one of which is the fact that radar detection systems are forced to function in conditions of interference of various natures, which cannot always be described by a Gaussian model. Thus, the paper focuses on the problem of detecting radar signals reflected from moving targets against the background of obstacles described by a non-Gaussian distribution, and also investigates ensuring the stability of the detection algorithm. The random occurrence of disturbances in the observation process allows for their mathematical representation to use an autoregression model generated by a disturbance described by the Laplace model. As a result, a locally optimal decision rule is synthesized for detecting a signal of a known form against the background of an autoregressive disturbance, which consists in finding the maximum following the signal parameter and the vector of the disturbance parameters of the obtained likelihood ratio, which is the ratio of the hypotheses put forward concerning the obtained sample. The robustness of the detection algorithm is ensured by estimating the unknown parameters of the noise process using an empirical Bayesian approach. In the course of work, the performance of the synthesized algorithm under the influence of non-Gaussian interference, which is described by the K-distribution model, was also investigated. To understand the effectiveness of the proposed detection algorithm, statistical modeling is performed. The simulation results confirm the effectiveness of the synthesized robust algorithm over the non-robust one, the effectiveness of which is manifested in the presence of impulse interference with an increase in the probability of their occurrence, accordingly, it is more resistant to the occurrence of chaotic impulse interference, while the non-robust algorithm gives significant errors, which leads to the deterioration of the detection of the useful signal. Радіолокаційне виявлення сигналів, що містять корисну інформацію про об’єкти спостереження, являє собою комплексний та багатофункціональний процес, що об’єднує розв’язок різних задач, однією з яких є виявлення рухомих цілей на тлі пасивних завад. ЇЇ вирішення ґрунтується на фундаментальній ідеї, яка спирається на застосування ефекту Допплера, відповідно до чого, було розроблено методи селекції рухомих цілей, які полягають в зміні частоти сигналу, відбитого від рухомого об’єкту. Традиційні алгоритми, черезперіодного віднімання пасивних завад систем СРЦ є ефективним, коли завади стаціонарні, проте мають недоліки, викликані завадами зі складним частотним спектром. В результаті для підвищення ефективності систем радіолокаційного виявлення було запропоновано та досліджено адаптивні алгоритми та показано їх переваги. Однак потреба в подальшому опрацюванні та удосконаленні існуючих систем та методів,залишається і досі актуальною, та пояснюється багатьма аспектами одним з яких є те, що системи радіолокаційного виявлення змушені функціонувати в умовах завад різної природи, що не завжди можна описати гаусівською моделлю. Таким чином, у роботі увага приділяється проблемі виявлення радіолокаційних сигналів, відбитих від рухомих цілей, на фоні завад, які описуються негаусівським розподілом, а також досліджується забезпечення стійкості алгоритму виявлення. Випадковість виникнення завади в процесі спостереження, дозволяє для їх математичного представлення використати авторегресійну модель, що генерується збуренням описаними моделлю Лапласа. В результаті виконується синтез локально-оптимального вирішального правила для виявлення сигналу відомої форми на тлі авторегресійної завади, який полягає у знаходженні максимуму відповідно до сигнального параметра і вектора параметрів завади отриманого відношення правдоподібності, що являє собою відношення висунутих щодо отриманої вибірки гіпотез. Стійкість алгоритму виявлення забезпечується шляхом оцінки невідомих параметрів шумового процесу при застосуванні емпіричного байєсівського підходу. В процесі роботи також досліджено працездатність синтезованого алгоритму при дії негаусівських завад, що описується моделлю K-розподілу. Для розуміння ефективності запропонованого алгоритму виявлення виконується статистичного моделювання. Результати моделювання підтверджують ефективність синтезованого робастного алгоритму перед неробастним, дієвість якого проявляються при наявності імпульсних завад із збільшенням ймовірності їх виникнення, відповідно він більш стійкий до виникнення хаотичних імпульсних завад, тоді як не робастний алгоритм дає значні похибки, що призводить до погіршення виявлення корисного сигналу.

When range ambiguity and range dependence coexist in forward-looking ground moving target indication (GMTI) radar, the traditional space-time adaptive processing (STAP) approaches may fail to work. To cope with this issue, a range-ambiguous clutter suppression approach based on elevation time diverse array (TDA) radar system and azimuth phase coding (APC) technique is presented in this paper. Due to the advantages of frequency scanning, the elevation TDA radar is able to cover all directions by transmitting a single time-shifted waveform. Therefore, a frequency bandpass filter is devised for each single range-bin to achieve the mainlobe echo extraction procedure. In the sequel, the azimuth phase decoding and Doppler filtering are performed to deal with the residual sidelobe ambiguous energy. With two-dimensional filtering, one can extract the unambiguous signals for each range region independently. Then, the traditional clutter compensation and STAP methods are employed to suppress the ground clutter. Finally, the effectiveness of the proposed framework in resolving range ambiguity are verified by the numerical simulation experiments.

Motivated by recent advances in deep learning, a novel deep complex-valued convolutional neural network (CV-CNN)-based method is proposed for ground moving target indication (GMTI) in a multichannel synthetic aperture radar (SAR) system. The proposed method integrates the SAR-GMTI task into a blind inverse problem solved by a deep CV-CNN named CV-GMTINet. To take advantage of the amplitude and phase information of complex multichannel SAR images, both feature maps and network parameters are extended into the complex domain. The proposed CV-GMTINet is designed by adopting complex-valued residual dense blocks (CV-RDBs) to adaptively learn complex hierarchical features. The trained CV-GMTINet, as a GMTI processor, can be applied to complex multichannel SAR images to discriminate moving targets from stationary clutter and refocus the moving target images simultaneously. Experiments on TerraSAR-X data show that the proposed method achieves significant improvements over existing state-of-the-art GMTI methods in both detection performance and refocusing accuracy, especially for the slow-moving target and the moving target with only along-track velocity.

Ground moving target indication (GMTI) has been extensively applied in modern warfare. In this letter, a novel deceptive jamming technique for countering multichannel synthetic aperture radar (SAR)-GMTI is presented, which is implemented with the frequency diverse array (FDA). Different from the phased array (PA), FDA modulates the transmitting signal with multiple frequencies across its array elements, which, consequently, enables range-dimension false target deception for SAR imaging. Furthermore, to achieve effective deception for the GMTI, micromotion modulation is adopted to simulate dynamic features of the moving targets. Mathematical derivation and simulation results show that the proposed technique can efficiently produce massive false targets in both the range and azimuth dimensions.

Video synthetic aperture radar (SAR) has been found very useful in ground moving target indication (GMTI) and tracking. The dynamic shadows in video SAR imagery sequences indicate the real positions of moving targets, which can be utilized in target detection and tracking. Unfortunately, the shadow-based method often fails when the shadows are not sufficiently developed. On the other hand, the traditional energy-based GMTI methods exhibit performance degradation when SAR images of a moving target are distorted or smeared. Neither of these two methods can stand alone to provide robust detection and tracking of moving targets. This article presents a joint processing framework for video SAR GMTI and tracking by combining the target shadow and echo energy information, which effectively lowers the false alarm and miss-detection rate. This approach is very suitable for real-time surveillance of ground moving targets in a single-channel video SAR system. The proposed approach has been verified by using the simulated and the real video SAR data.

In this article, an effective clutter suppression algorithm is presented for the space-borne azimuth multiantenna high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system, which is based on the null-space technique. First, the echo of bistatic geosynchronous-low earth orbit (GEO-LEO) azimuth multiantenna HRWS SAR-ground moving target indication (GMTI) system is utilized to deduce the coarse-focused image of moving targets and clutter, where the Chirp Fourier transform (CFT) in azimuth is involved. Then, the matrix form can be utilized to describe the coarse-focused image of the multiantenna SAR system and the corresponding covariance matrix can be estimated. After that, the null-space is constructed using the covariance matrix corresponding to clutter, where at least a redundant channel freedom is required. Since the null-space vector is orthogonal to signal-space vector, it can be used to suppress the clutter. As an equivalent phase is brought by the slant velocity, the moving targets' echo can be preserved during clutter suppression. Then, the optimization and suboptimization vectors for clutter suppression are introduced. It is worth noting that the proposed clutter suppression algorithm is robust for the antenna mismatch, that is, the antenna mismatch in phase and the corresponding position error. Finally, the theoretical investigations are validated using some simulation experiments, where the experiments for bistatic GEO-LEO and single-platform azimuth multiantenna HRWS SAR-GMTI system are both included. In addition, the real measured single-platform azimuth multiantenna HRWS SAR data experiments are also performed.

In multichannel high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) ground moving target indication (GMTI) systems, Doppler centroid (DC) is an essential parameter for spectrum reconstruction and image focusing. However, the conventional DC estimator faces many problems in moving target with multichannel SAR system, such as nonuniform data in azimuth, subsignal usage, and channel mismatch. To estimate the DC of moving target, the modified cross-correlation coefficient (MCCC) method for multichannel SAR system is proposed in this letter, which is especially suitable for nonuniform and undersampled data. Simulations with real spaceborne data demonstrate the effectiveness of this method.

The inherent magnitude and phase errors between channels in the multichannel synthetic aperture radar/ground moving target indication (SAR/GMTI) system will affect the clutter suppression and moving target detection performance of the system. To solve this problem, this letter proposes an adaptive channel balancing algorithm based on the 2-D Gaussian kernel function. First, the iterative reweighted least squares (IRLS) algorithm is used to estimate and compensate the inherent linear phase error that varies with the Doppler frequency in the range-Doppler domain. Then the Gaussian statistical analysis of the residual phase errors is performed in the image domain after the azimuth processing. According to the coupling characteristics of the range and azimuth of the SAR image, the correlation coefficient and the standard deviations of the phase errors in the range and azimuth directions are computed, and a 2-D Gaussian kernel function is constructed to estimate and compensate the residual phase errors. Finally, a mean filter is used to compensate the 2-D magnitude errors. This algorithm can eliminate the magnitude and phase errors and improve the coherence between different channels. The real airborne SAR data demonstrate the effectiveness of the proposed algorithm.

Synthetic aperture radar (SAR) systems have extensively contributed to diverse scientific applications in the recent past decades. For the next generation of top-level SAR systems, the multimodal operation of wide swath imaging, sliding spotlight, and highly sensitive ground moving target indication (GMTI) is becoming increasingly crucial. Following this concept, in this article, a novel airborne multiple-input-multiple-output (MIMO) SAR system, along with the space-time coding (STC) waveform scheme and the phased array, is developed to realize the multimodal operation. In particular, the airborne MIMO SAR system consists of two transmitters and four receivers. The signals of the multiple modes, which overlap in the frequency domain, are interpulse modulated and transmitted by different subarrays concomitantly. The superposed echoes are separated by range-Doppler filtering. Based on the separated echoes, we can obtain eight equivalent transceiver channels and, as a result, enhance the signal-to-noise ratio (SNR) for airborne multimodal operation. In the future, the STC waveform scheme and flight experiments of this novel airborne MIMO SAR system will be further investigated to reduce the pulse repeat frequency (PRF) for spaceborne multimodal operation.

In a multichannel synthetic aperture radar (SAR) system, because of the target uncooperative motion, a ground moving target (GMT) is usually smeared, distorted, and shifted in an SAR image. In this article, a novel approach for multichannel SAR-GMT indication (GMTI) processing is proposed. The main innovations of this method are that a GMT can be well refocused and relocated since the target high-order Doppler parameters can be precisely estimated based on a high-order polynomial phase signal (PPS) model, and the target statistical amplitude and phase information is jointly applied to improve the radial velocity estimation robustness. Compared with the current SAR-GMTI algorithms, the improvements of this method over the existing methods are: 1) the topography interferometric phase can be effectively compensated by applying an adaptive 2-D spectrum filtering technique via iterative processing; 2) a GMT can be well imaged since the target Doppler chirp rate and the quadratic chirp rate can be well estimated via the 2-D coherent integration in the time-frequency plane; and 3) a GMT can be precisely relocated into its original position by applying the generalized amplitude and phase weighting technique. Real-measured SAR data processing results are presented to validate the effectiveness and feasibility of the proposed method.

High-resolution wide-swath (HRWS) multichannel synthetic aperture radar (SAR) system possesses a number of receiving channels along the azimuth direction, so it has the capacity of moving target indication and imaging. However, due to the radial velocity of the moving target, false targets occur in the focused image. By estimating the radial velocity and combining it with moving target imaging, false targets can be effectively suppressed. In this letter, a method of radial velocity estimation of a moving target is proposed based on the theory of subspace projection. This method does not need to estimate the real azimuth position of the moving target and can predict the processing time. Simulation and airborne measured data show the effectiveness of the proposed method.

Clutter suppression is a challenging task in synthetic aperture radar-ground moving target indication (SAR-GMTI). In general, sufficient secondary samples are not easily acquired due to the non-stationary and non-homogeneous characteristics of bistatic SAR (BiSAR) clutter, resulting in worse clutter suppression results. Recently, space-time adaptive processing based on sparse recovery (SR-STAP) has been developed since its better clutter suppression performance with less samples. However, since the off-grid problem in space-time domain caused by BiSARs separate configuration, existing SR-STAP would suffer from severe performance degradation. To address this problem, a clutter-ridge matched STAP (CRM-STAP) method for BiSAR non-stationary clutter suppression is proposed. First, clutter distribution modeling with arbitrary BiSAR configuration is applied to accurately obtain the clutter ridge in space-time domain. Then, keystone transform and time-division processing are applied to correct range cell migration and eliminate Doppler frequency migration, respectively. Next, to solve the off-grid problem, the CRM dictionary is reconstructed via adaptive gradient method, which is established along the direction of clutter ridge and its orthogonal direction. Then, with the constructed CRM dictionary, the clutter covariance matrix (CCM) estimation process is transformed to a multi-measured vector optimization problem, and it can be directly solved by the sparse Bayesian learning algorithm. Finally, based on the estimated CCM, the CRM-STAP filter is built to suppress the non-stationary clutter effectively. Compared with the existing STAP and SR-STAP methods, this method can avoid the performance degradation in clutter suppression caused by the off-grid problem and overcomes the strong non-stationary problem of BiSAR clutter in heterogeneous environments. In October 2020, we have successfully carried out the world’s first airborne BiSAR-GMTI experiment, and the experimental results are given to verify the effectiveness of this method.

In this letter, a novel method is proposed to estimate the along-track baseline for a multichannel synthetic aperture radar (SAR) system, intended for ground moving target indication (GMTI) applications. First, an adaptive spectrum filtering technique is proposed to compensate the terrain interferometric phase caused by the cross-track baseline and then the ground clutter is rejected by applying the joint-pixel displaced phase center antenna (JPDPCA). After performing the moving target detection, the target radial velocity is estimated according to the azimuth position offset by exploiting the road-aided information. Finally, the along-track baseline is derived based on the subspace projection (SP). The effectiveness of the proposed method is validated by data from the experimental airborne system.

Anti-ship missiles are a primary threat to naval targets because sea-skimming tactics enable the missile to approach the vessel while being obscured by sea clutter. In order to counteract this serious danger, the ship’s radar employs moving target indicator processing in order to detect and subsequently start the tracking of the incoming missile. In this paper, we set up a relevant simulation in order to examine the moving target indicator process for a naval scenario at horizon range which gives the greatest counter-reaction time, which usually is for very small grazing angles in a sea cluttered environment. Finally, the results of the simulator are beneficial to radar designers and educational professionals.

Moving target indication (MTI) is considered one of the most important applications of synthetic aperture radar (SAR) in military operations and traffic monitoring. Although many studies have focused on detectors based on the statistical clutter models, a mismatch between the measured data and the applied statistical model often leads to unreliable MTI results, particularly in terms of false alarm rates. To reduce the number of false alarms, we propose an efficient MTI scheme consisting of conventional MTI techniques (the displaced phase center antenna (DPCA) and the interferogram’s magnitude and phase (IMP) methods), which operate in dual-receive antenna (DRA) mode in an SAR system. These techniques are coupled with a new discrimination stage based on a new detection metric—weighted kurtosis. Using simulated and real measured data from TerraSAR-X, the proposed MTI scheme demonstrates robust performance in the reduction of false alarm rates with only a slight increase in the computation time compared with conventional schemes.

Moving-target indication (MTI) has been widely researched in urban sensing. The existing research work mainly concentrates on a single moving target, and there exist difficulties to research multiple moving targets, such as estimating the number of targets and distinguishing the targets that are close to each other. In this letter, based on compressive sensing (CS), we consider the indication of multiple moving targets in through-the-wall radar imaging (TWRI). Using the method of change detection, stationary targets and clutter are removed. Then, according to the reasonable state of motion, we delete the ghost points and combine the tracing points of each moving target. Comparison with the existing CS-based MTI method shows that with the proposed method, ghosts can be suppressed for about 60% at most, which can better estimate the number of moving targets and show the indication of each moving target.

To effectively generate a verisimilar false moving target against a three-channel synthetic aperture radar ground moving target indication (SAR GMTI), a scattered wave deceptive jamming method performed by the time-delay modulation and the phase modulation (STP) is first proposed by using traditional deceptive jamming strategies. In addition, a novel three-channel short-time SAR GMTI method (TCSTSG) is heuristically inspired by the short-time Fourier transform (STFT) method and proposed with anti-jamming capacity. The procedure of the TCSTSG method is to divide the whole-time data into shorter segments, and each segment will be processed by displaced phase center antenna (DPCA) technique and along-track interferometry (ATI) technique. With the TCSTSG method, the moving target will have different positions in each segment. However, the false target generated by the traditional deceptive jamming method based on multiple jammers (DJ-MJ) and the STP method has the same position in each segment and a verisimilar false moving target cannot be generated. To meet this challenge, an improved scattered wave deceptive jamming method based on the moving jammer beam footprint (SMJBF) is proposed. Compared with the traditional scattered wave jamming methods, the improvement is that the jammer can control the azimuth position and velocity of the footprint. The position and velocity are calculated by comparing the detection results of the moving target and those of the false target with the TCSTSG method. Then the false target will be similar to the moving target. Finally, simulations are provided to verify the effectiveness of the proposed methods.

Imaging of ground multiple moving targets in a synthetic aperture radar (SAR) system is a challenging task due to the fact that targets are defocused owing to motions and contaminated by the strong background clutter. Motivated by recent advances in deep learning, a novel deep convolutional neural network (CNN)-based method, DeepImaging, is proposed for ground moving target imaging (GMTIm). Different from conventional imaging methods relying on the prior knowledge of imaging, the proposed DeepImaging is directly trained to learn an implicit imaging model of multiple moving targets. It is free of motion parameter estimation and iteration process. Then, the trained DeepImaging, as an imaging processor, can be applied to the SAR complex received data after clutter suppression to achieve the multiple moving target imaging and the residual clutter elimination simultaneously. Simulations and experiments on the Gotcha data show that the proposed method achieves significant improvements over existing state-of-the-art GMTIm methods in terms of imaging quality and efficiency.

Azimuth ambiguity degrades the quality of synthetic aperture radar (SAR) images and leads to the increase of false alarm rate in ground moving target indication (GMTI). Due to the existing azimuth ambiguity, suppression methods do not remove the first-order ambiguity completely and ignore the ambiguity above first order as well, moving target detection is affected by residual ambiguity. Hence, a novel method to suppress first-order and higher order azimuth ambiguities for the dual-channel SAR/GMTI is proposed in this letter. First, the displaced phase center antenna (DPCA) technique is applied to suppress clutter. Then, estimate the local azimuth ambiguity-to-signal ratio (LAASR) to find out the area affected by ambiguity. Finally, an inpainting algorithm is improved to patch the ambiguity area. The proposed method can remove the ambiguity almost completely. Moreover, the method is verified using the GaoFen-3 SAR dual-channel complex image data, and the result shows that the false alarm of moving target detection is degraded without reducing the detection rate.

Traditional deceptive jamming methods effectively generate false targets against three-channel synthetic aperture radar ground moving target indication (SAR GMTI). However, effectiveness of traditional methods is limited to azimuth distance between two jammers. When the distance requirement is not satisfied, the jamming amplitude coefficients will be too high to be used by jammers. To overcome this disadvantage, a novel multiple-jammer deceptive jamming method is proposed. The intercepted radar signal is retransmitted and performed by amplitude and time-delay modulation. Then, jamming signal is first modeled as a polynomial function where the polynomial order is equal to the number of jammers. The interferometric phase difference between a moving target and a false target is utilized as an objective function, the azimuth relocated position difference between a moving target and a false target is utilized as the constraint to reduce computational burden, and the optimal jamming amplitude coefficients are estimated by solving the optimization problem based on the particle swarm optimization (PSO). Hereby, the optimal jamming amplitude coefficients and gain calculated with the proposed method will be lower than those calculated with traditional methods when the distance requirement is not satisfied. By utilizing the optimal coefficients and jamming amplitude gain, a false target generated with the proposed method will be detected by displaced phase center antenna (DPCA) technique and be relocated in the desired position by along-track interferometry (ATI) technique. Experimental results verify the effectiveness of the proposed method.

In this paper, we present an approach for ground moving target imaging (GMTI) and velocity recovery using synthetic aperture radar. We formulate the GMTI problem as the recovery of a phase-space reflectivity (PSR) function which represents the strengths and velocities of the scatterers in a scene of interest. We show that the discretized PSR matrix can be decomposed into a rank-one, and a highly sparse component corresponding to the stationary and moving scatterers, respectively. We then recover the two distinct components by solving a constrained optimization problem that admits computationally efficient convex solvers within the proximal gradient descent and alternating direction method of multipliers frameworks. Using the structural properties of the PSR matrix, we alleviate the computationally expensive steps associated with rank-constraints, such as singular value thresholding. Our optimization-based approach has several advantages over state-of-the-art GMTI methods, including computational efficiency, applicability to dense target environments, and arbitrary imaging configurations. We present extensive simulations to assess the robustness of our approach to both additive noise and clutter, with increasing number of moving targets. We show that both solvers perform well in dense moving target environments, and low-signal-to-clutter ratios without the need for additional clutter suppression techniques., Comment: Submitted to IEEE Transactions on Computational Imaging

This paper presents a novel moving target deception jamming method against synthetic aperture radar (SAR)-ground moving target indication (GMTI) based on fast azimuth-domain block EM calculation. In this method, a fast electromagnetic(EM) calculation based on equivalent bistatic scattered fields is used to simulate the scattering characteristics of moving false target, which greatly improves the computational efficiency. With the azimuth-domain block implementation, the jammer’s frequency response (JFR) can be calculated by fast searching and interpolating with EM database, and the computation load is further reduced. The implementation and the performance of the proposed method are discussed. Comparing with the traditional methods based on a point model, the proposed method is able to generate more verisimilar moving false targets for different attitudes and resolutions. The simulation experiments are provided to demonstrate the validity of the proposed method.

A range-ambiguous clutter suppression approach based on extended azimuth phase coding (EAPC) is proposed to handle the range ambiguity of the multiple-input multiple-output synthetic aperture radar (MIMO-SAR) system for ground moving target indication (GMTI) application. The echoes from different ambiguous range regions can be well separated in the transmit spatial frequency domain by properly designing the EAPC shift factor. In the sequel, a set of transmit filters are employed to extract the echoes of each ambiguous region independently. Then the azimuth deramp operation is applied to the extracted data to focus the target energy of the desired region while the residual target energy of other regions is still smeared due to the mismatched reference function. After this, the adaptive matched filtering algorithm is adopted to suppress the clutter and detect the moving target. Finally, numerical simulation experiments are presented to demonstrate that the developed framework can obtain good results for range-ambiguous clutter suppression and ground moving target detection.

Video synthetic aperture radar (SAR) has been found to be very valuable for detection and tracking of slow moving targets and for observing changes over short time periods. Shadows produced by target motion in sequential radar images can be used to detect targets themselves. This article presents a framework for shadow-aided moving target detection using deep neural networks (DNNs) in video SAR. The faster region-based convolutional neural network (Faster-RCNN) is first used to detect shadows in a single frame, and then all the detections in each frame are filtered by the improved density-based clustering algorithm to mitigate false alarms. The missing alarm in detection can be suppressed by using the designed bi-directional long-short-term memory (Bi-LSTM) network. The proposed approaches have been tested by both the simulated and the real data, and a comparison to classical processing is given. This article reveals that the DNN techniques are very suitable for video SAR moving target detection.

Clutter background suppression and velocity estimation for moving targets are two critical problems in synthetic aperture radar–ground moving target indication (SAR-GMTI). A robust principal component analysis (RPCA) method is used to separate the sparse matrix of moving targets from the low-rank matrix of static backgrounds by using amplitude information in the image domain. However, the nonsparsity of the moving target echoes limits the performance of the RPCA in SAR-GMTI, and the velocity of the moving target cannot be estimated since the phase information is destroyed by the soft-thresholding operator in the RPCA process. To solve these problems, a novel moving target detection method based on RPCA (NRPCA) for SAR systems is proposed in this article. An atomic norm-based optimization program is first constructed to transform the data sparsity requirement into a moving target sparsity requirement. Although this optimization program is NP-hard, it is transformed to semidefinite programming by relaxation. Furthermore, accurate velocity estimation is performed using dual function theory and the alternating direction method of multipliers (ADMM) algorithm while the selection of the sparsity order k is avoided. Simulations and analyses based on experimental data illustrate the effectiveness of the proposed method.

Clutter background suppression is a critical problem in synthetic aperture radar-ground moving target indication (SAR-GMTI). In general, a great quantity of secondary data is not easily acquired in heterogeneous environments. To solve the problem of clutter suppression, a method based on nonlocal self-similarity-robust principal component analysis (NSS-RPCA) is proposed for airborne SAR systems. First, discrete clutter is separated from the echo data by RPCA after range pulse compression. Second, similar blocks of the residual-clutter background are extracted to overcome the training sample limitation using the NSS method in the 2-D time domain. Third, subcovariance matrices are structured by the similar blocks, and the subcovariance matrix is stacked into a tensor. Then, the subclutter covariance matrix can be obtained from the stacked subcovariance matrix tensor by RPCA, where the residual-clutter tensor is of low rank and the target tensor is sparse. Finally, the residual clutter can be suppressed by the subclutter covariance matrix. In this manner, the source of independent identically distributed (IID) samples will be increased significantly without aperture loss by the proposed method. Simulation and analysis based on the experimental data illustrate the effectiveness of the proposed method.

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

An important problem is how to generate false moving targets, whose relocated azimuth position is similar to that of real moving targets. To solve this problem, an interrupted sampling scattered wave deception jamming method against three-channel synthetic aperture radar ground moving target indication (SAR GMTI) is proposed. A stationary jammer uses a controllable jammer antenna to generate verisimilar moving targets by controlling velocity and initial position of jammer beam footprint. The antenna sampled moves along the different tracks. For each track, the slant history of jamming signal is changed varying with different pulse recurrence intervals (PRI), and the movement of the footprint will introduce a Doppler frequency in jamming the signal. By analyzing parameters’ difference between echoes and jamming signal, the velocity and the initial position of the footprint will be calculated, and then the verisimilar false targets are generated. The effectiveness of the method is verified by simulation experiments.

Along-track interferometric (ATI) synthetic aperture radar (SAR) has proven a powerful tool for ground-moving target indication (GMTI). ATI-SAR systems use more than one antenna (at least two), mounted on a moving platform and displaced along the moving direction. Each SAR antenna provides a complex image of the same ground scene acquired with a very small time delay, depending on the separation (baseline) between the antennas. Starting from these interferometric images, ground-moving targets can be detected and their radial velocity estimated.

Much research from open literature shows that polarization diversity can provide another dimension which may be exploited to improve the performance in ground moving target indication (GMTI), compared with space–time adaptive processing (STAP). In this article, we report the multichannel synthetic aperture radar (SAR)-based GMTI (SAR-GMTI) experiment and its preliminary results with a N-SAR system which is an airborne quadrature-polarimetric (quad-pol) radar system. First, the joint polarization-space adaptive processing (JPolSAP) is performed in the image level, but two suboptimal versions of JPolSAP, where the polarimetric matched filter (PMF) vector and the full-one vector are exploited to substitute for the polarimetric steering vector, respectively, are evaluated since the polarimetric steering vector of the moving target is unknown precisely in practical applications. Then, considering the computational complexity and lack of secondary data in a inhomogeneous environment due to high degrees of freedom of the JPolSAP processor, two cascade processors are evaluated, including the polarization enhancement that uses PMF and noncoherence integration detection (NCID) technique. Furthermore, we utilize the polarization information to accomplish SAR terrain classification, and subsequently secondary data from the same scattering type clutter can be obtained for clutter suppression under the guidance of polarization classification results as a priori knowledge. Finally, the experimental results demonstrate that: 1) the suboptimal JPolSAP processor with PMF steering vector can effectively enhance GMTI performance about 13 dB (or even up to 5 dB) relative to the worst (or best) single-polarization (S-pol) processor case and has the best robustness compared with the one with full-one steering vector; 2) polarization enhancement using PMF also obtains a good output gain of polarization filter, especially for quad-pol enhancement, which gains up to 2–3 dB with respect to the best output of S-pol processor, and the NCID technique can obtain good performance of moving-target detection; and 3) under the guidance of polarization classification results, the capability of clutter suppression can improve even up to 15 dB with respect to the one without classification.

Magnetron-based marine radar technology is mature, affordable, reliable, and very effective for maritime safety applications. Commercial systems may be procured at a modest cost as compared to fully coherent solid-state systems. Magnetron oscillators inherently generate random phase signals. Phase instability on a pulse-to-pulse basis impedes this class of marine radars from success in applications requiring coherency such as moving target indication (MTI) or in generating target imagery. This limitation may be overcome by incorporating radio frequency sampling and cross-correlation of the transmit and receive signal technology to augment the current capability of available systems. In this research, the pulse train on transmit and receive is correlated in order to reject interference and detect image targets. Sampling the transmit signal and target echo on receive permits fully coherent processing. Marine radars traditionally operate non-coherently, and as such, offer limited surveillance in clutter rich environments. In this study, the authors report on a non-coherent marine radar that has been modified to produce a pseudo-coherent or coherent-on-receive sensor system. This is crucial to MTI and target image formation. In laboratory experiments, they employed a magnetron oscillator-based system to generate an inverse synthetic aperture radar image. The image was formed using four different algorithms: filtered back-projection (FBP), time domain back-projection (TDBP), an algebraic reconstruction technique, and frequency domain back-projection. In their research, TDBP produces exquisite imagery of steel rods, and it is the standard developed in this study. FBP performed poorly as compared to all other algorithms.

In multi-channel synthetic aperture radar-ground moving target indication (SAR-GMTI), most radial velocity estimation methods are based on the phase difference between channels. However, the image coregistration and channel phase errors will have a severe impact on the phase difference between channels. Moreover, it deteriorates the performance of the moving target radial velocity estimation. To solve this problem, a robust radial velocity estimation method is proposed using a frequency diverse array-SAR (FDA-SAR) in this letter. By introducing the step frequency, the interferometric phase among channels is a linear function of the Doppler frequency. The radial velocity of moving targets is embedded in the first-order term of the linear function. Meanwhile, the first-order term does not include channel phase error terms. Therefore, the accurate velocity of moving targets is estimated by the first-order coefficient which is solved by the least-squares fitting method. Afterward, according to the analysis and derivation, the proposed method is robust on the condition of image coregistration error. At last, simulations and data analysis illustrate the effectiveness of the proposed method.

Displaced phase center array (DPCA) and along-track interferometry (ATI) have been widely used for ground moving target indication (GMTI) in multi-channel circular synthetic aperture radar (CSAR) system. In ideal conditions, these methods can suppress background clutter effectively. However, the nonideal movement of platform may create a nonzero crab angle, which will induce a cross-track baseline offset and affect GMTI processing seriously. This letter analyzes the influence of crab angle on interferometric phase theoretically. Here, we propose a two-step phase calibration method to greatly improve the performance of CSAR-GMTI by eliminating the range-varying phase error. This method is independent of imaging and GMTI processing. Simulated and measured data results verify the correctness of the theoretical analysis and the validity of the proposed method.

This letter presents a novel scheme for moving target deception jamming (MTDJ) against multichannel synthetic aperture radar (SAR)-ground moving target indication (GMTI) based on multiple jammers. The multichannel SAR signal models for the real and false moving targets are established first. Then, the proposed MTDJ method based on multiple jammers is derived according to the principle that the interferometric phase matches the across-track velocity of the moving target. To ensure efficient deception jamming and to resolve the accurate scattering coefficient modulated in each jammer, the interferometric phase of the false moving target is decomposed into two parts, one is the term that matches the across-track velocity, while the other is the additional term generated by each jammer. Hence, the expected modulation coefficient in each jammer can be determined based on the additional interference phase cancellation. Finally, the proposed MTDJ is realized by generating the jamming signals in each jammer via the efficient deception jamming technique. The simulation results demonstrate the effectiveness of the proposed MTDJ method.

Moving target detection is of vital importance to maritime security and maritime resource protection. However, the detection of slow or weak targets is difficult based on traditional methods. A new detection method is proposed by using the different motion variations of radar moving target and sea clutter in the range-Doppler spectrum sequence. The first step in implementing this method is the separation of moving target and sea clutter by the low-rank representation, in which the target and clutter are modeled as foreground and background components. Subsequently, a sea clutter discriminator is constructed within the sea clutter bandwidth to further remove the sea clutter (false alarms) that exists in the foreground. The proposed method can reduce the sea clutter power while maintaining the target power and improve the detection rate of moving targets, especially slow or weak targets. Data collected with airborne maritime surveillance radar in maritime moving target indication (MMTI) mode are used to validate the performance of the proposed method. The experimental results demonstrate that the improvement in the signal-to-clutter ratio (SCR) obtained with the proposed method is better than that obtained with space-time adaptive processing (STAP, including 1DT-STAP, 3DT-STAP and sparse-STAP) and principal component pursuit (PCP) methods; additionally, the figure of merit (FOM) of the proposed method is higher than that of the constant false alarm rate (CFAR) and PCP method. Furthermore, the tracks of ships are obtained by applying a location constraint to the foreground sequence.

High-resolution and wide-swath (HRWS) is an important challenge in synthetic aperture radar (SAR). Combined SAR with array, the azimuth dual-channel SAR configuration achieves high azimuth resolution and HRWS in low Pulse repetition frequency (PRF) by doppler band synthesizing technology. However, low PRF introduces ambiguity to ground moving target indication (GMTI). Interferometry is performed between two look data in the range frequency domain to resolve the ambiguity by decreasing the interferometry phase. As a result, the phase becomes insensitive to the radial velocity of moving targets. This condition brings difficultly in moving target detection. Two channels together with two equivalent channels are exploited fully to solve these problems. The four-channel interferometry is also conducted to improve the GMTI performance. Parameters, including system and motion parameters, are estimated unambiguously to complete HRWS imaging and GMTI. System parameter design is also considered to satisfy the requirements of HRWS and GMTI simultaneously. Simulations demonstrate the validity of the proposed methods.

Only a few publications exist at present on heterogeneous track-to-track fusion (T2TF). A common limitation of the current work on heterogeneous T2TF is that the cross covariance due to common process noise cannot be computed. This is due to the fact that two local trackers use different dynamic models, and hence, it is difficult to account for the common process noise. We consider a heterogeneous T2TF problem in three dimension (3-D) using a passive infrared search and track (IRST) sensor and an active air moving target indicator (AMTI) radar with the nearly constant velocity motion of the target. The active AMTI tracker uses the Cartesian state vector with 3-D position and velocity, and the dynamic model is linear. A passive IRST tracker commonly uses modified spherical coordinates (MSCs) for the state vector, where the dynamic model is nonlinear. In this formulation, the common process noise is explicitly modeled in both dynamic models. Therefore, it is possible to take into account the common process noise. We use the cubature Kalman filter (CKF) in both trackers due to its numerical stability and improved state estimation accuracy over existing nonlinear filters. The passive tracker uses a range-parameterized MSC-based CKF, and the active tracker uses a Cartesian CKF. We perform T2TF using the information filter (IF), where each local tracker sends its information matrix and the corresponding information state estimate to the fusion center. The IF handles the common process noise in an approximate way. Results from Monte Carlo simulations show that the accuracy of the proposed IF-based T2TF is close to that of the centralized fusion with varying levels of process noise and communication data rate.

Ground moving target indicator (GMTI) RADAR system frequently incorporates space-time adaptive processing for successful moving targets discrimination in a highly cluttered environment. The minimum detectable velocity (MDV) of the target gets affected due to the added clutter impairments such as foliage penetration and angle independent channel mismatch. A novel clutter model is postulated in this work that includes assessment enacted in view of both angle and Doppler clutter impairments at a time instead of their individual analysis as in the reported literature. The performance of the GMTI RADAR in terms of the signal-to-interference-plus-noise ratio loss is evaluated using the extrusive beamforming methods namely sample matrix inversion and principal component canceller. In high decorrelated clutter environment, the later outperforms in terms of the MDV, making use of a lesser number of training snapshots. The approach is validated with mathematical modelling and numerical simulations.

In this paper, a novel method is proposed to achieve high range resolution imaging with orthogonal sub-band frequency diverse array. In the proposed system, the signals transmitted by adjacent subarray has a frequency increment which is close to signal bandwidth. When transmits narrow band signals, this system has the ability of Moving Target Indication (MTI). Meanwhile, this system is capable of high-resolution imaging by splicing spectrum technique. This paper focuses on the application of the proposed method in SAR imaging to obtain high range resolution imaging by spectrum splicing. Simulation results have verified the effectiveness of the proposed method.