This paper explores the mitigation of mainlobe deceptive jammers using a phase-coded planar array. At the modelling stage, a Two-Dimensional (2D) phase coding scheme is employed in the transmit planar array, along with the transmission of Linear Frequency Modulated (LFM) waveform in a planar array. Such a phase-coded planar array not only preserves the waveform properties, but also provides enhanced controllable beamforming capabilities in elevation-azimuth-range 3D domain. In the receiver, by separating and compensating the transmitted signals, the mainlobe deceptive jammers occurring in different range ambiguous regions, are discriminated and suppressed. At the analysis stage, the Capon spectra and spatial filtering results are provided to verify the effectiveness of mainlobe jammer mitigation with the proposed phase-coded planar array.

This letter presents an optimization approach for frequency increments tailored to Frequency Diverse Array (FDA)-Multiple-Input Multiple-Output (MIMO) radar for target localization. We start to formulate the problem as minimizing the Cramér-Rao Bounds (CRBs) for both range and angle estimation, subject to practical constraints on the frequency increments. To facilitate optimization, the objective function is mathematically transformed, which results in a maximization problem, leveraging its inherent non-negativity of both the numerator and denominator. To address the resultant non-convex and NP-hard optimization problem, a Minorization-Maximization (MM)-Maximum Block Improvement (MBI) algorithm is devised by partitioning the frequency increment vector into distinct blocks, allowing for alternating maximization. In particular, each frequency increment is refined with the MM algorithm, while holding the others fixed, and only the block yielding the maximum objective increment is updated within each iteration. Simulation results are provided to demonstrate the excellent target localization of our proposed approach.

This paper deals with the simultaneous adaptive target detection and parameter estimation utilizing a Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radar. At the design stage, a linearized model of the received signal is derived, leveraging the centro-Hermitian (persymmetric) structure of the array manifold and considering potential steering vector mismatches related to the actual target parameters. Exploiting this model, the detection problem is formulated and tackled leveraging adaptive detection approaches, resorting to the Persymmetric Generalized Likelihood Ratio (PGLR) and Persymmetric Adaptive Matched Filter (PAMF), respectively. However, they demand the Maximum Likelihood (ML) estimates of target incremental range (i.e., the deviation of the target actual position from the center of the range bin) and angle. Two iterative methods are devised to solve the ML estimation problem. The former is based on Dinkelbach's algorithm, which is capable of achieving the global optimum, whereas the latter is a fast-converging alternative approach relying on the Coordinate Descent (CD) framework. Numerical results highlight the effectiveness of the proposed simultaneous detection and estimation strategies, also in comparison with suitable benchmarks and counterparts.

This paper aims to suppress mainlobe deceptive jammers considering the multipath effect in a frequency diverse array-multiple-input multiple-output (FDA-MIMO) radar. At the problem formulation stage, the overall received signal including the true target, main-lobe deceptive jammers, and burst jamming signal in the presence of multipath propagation, is represented as a "low-rank + low-rank + sparse" decomposition model. Then, an improved Go Decomposition (GoDec) algorithm is developed to recover the components corresponding to the target signal and disturbance (including the mainlobe jammers and burst jamming signal). Furthermore, a data-dependent beamforming approach is implemented to eliminate the mainlobe deceptive jammers, where the covariance matrix is constructed using the recovered disturbance components, and the steering vector is obtained with a priori knowledge of the target position. Numerical results are provided to demonstrate the effectiveness of the devised technique and its superiority over competing methods in suppressing mainlobe deceptive jammers under multipath environments.

This letter proposes an iterative, fast, and efficient single-snapshot range and angle estimation method for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. The proposed approach transforms the angle and range estimation problem into the transmit and receive frequency estimation, and a 2-D frequency estimation algorithm is designed for multiple sinusoids (corresponding to multiple targets in radar). The frequency estimation is divided into coarse estimation and fine estimation. The coarse estimation can be achieved using the discrete Fourier transform, and fine estimation is estimated by the designed iterative structure. The designed algorithm achieves effective target parameters’ estimation with low computational cost. Simulation results demonstrate the effectiveness of the algorithm under various situations.

Ground moving target detection is one of the foremost tasks for multichannel synthetic aperture radar (SAR) system. The traditional robust principal component analysis (RPCA) method is capable of separating low-rank and sparse components from mixed echo signals, and it has been widely applied in SAR ground moving target indication (GMTI). However, it suffers from sensitivity to channel mismatch, high computational complexity, and excessively high false alarm rates. To address these issues, a novel method that combines adaptive multichannel data reconstruction (DR) with improved pseudo-skeleton decomposition (IPSD) is proposed. First, the iterative weighted approach is presented to precisely reconstruct the multichannel data vector with the joint-pixel model. After that, IPSD is presented to achieve the moving target detection, in which the row and column index sets are selected using the generalized inner product (GIP) and the amplitude histogram distribution criterion. Compared to the existing algorithms, the proposed algorithm effectively addresses the challenge of improving local region coherence in multichannel image sequences. In addition, compared to previous RPCA methods, the proposed algorithm significantly reduces false alarm rates in strong clutter backgrounds while achieving higher efficiency. Simulation results and real SAR data experiments validate the effectiveness of the proposed algorithm.

Using spaceborne transmitter and airborne receiver, the space–air bistatic radar (SABR) system has advantages of wide coverage, antistealth, and anti-interference. However, the SABR encounters severe clutter spreading in spatial–temporal domain since it works in down-looking mode, which would cause serious performance degradation in moving target detection. Moreover, SABR encounters several practical factors, such as the separated transmitter and receiver, the non-side-looking array configuration, wide Earth coverage, and Earth rotation, which would induce range-dependent and high-order nonlinear spatial–temporal-coupled ground/sea clutter. Under this circumstance, this article first investigates the general space-time clutter model of SABR for arbitrary transmitter–receiver geometry and array configuration. Then, the high-order nonlinear coupling relationship of clutter curve is derived, where the expressions of the spatial frequency and Doppler frequency are derived with respect to the eccentric anomaly of bistatic iso-range ring. In the sequel, the spatial–temporal-coupled and range-dependent characteristics of the clutter in SABR is analyzed and discussed in detail for three typical geometrical configurations with high/medium/low elliptical orbit satellite as transmitter and aircraft as receiver. On the basis of the above analysis, an effective clutter compensation approach based on subspace rotation is proposed to eliminate the range dependence of the clutter. Numerical examples of different bistatic geometries are conducted to analyze the spatial–temporal coupling relationship of clutter and validate the effectiveness of the proposed compensation method.

Frequency diverse array (FDA) introduces frequency increment across array elements, resulting in increased controllable degrees-of-freedom (DOFs) in spatial and frequency domains. As different sub-band waveforms are transmitted by different elements simultaneously, it is capable of obtaining a large synthesized bandwidth at the receiver, thus improving the resolution of radar imaging. In this article, a general bandwidth synthesis approach is proposed, which includes flexible shift of each sub-band waveform as well as combination of all these sub-band waveforms in frequency domain. Since the sub-band waveforms can be flexibly shifted in frequency domain, we can obtain an arbitrarily synthesized bandwidth, both wideband and narrowband waveforms. In the proposed approach, the general bandwidth synthesis mechanism is revealed. The analytical expressions of the bandwidth-synthesized signals are derived with an arbitrary shift in frequency domain, resulting in multiresolution synthetic aperture radar (SAR) imaging, which provides the potential for realizing multimode SAR imaging simultaneously. Several specific cases regarding the bandwidth-synthesized signals, including overlapped and non-overlapped, are provided to further explain the bandwidth synthesis approach. The range resolution, peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR) performance are analyzed. Nonuniform frequency shifts are applied to alleviate the PSLR degradation due to non-overlapped sub-band synthesis. Numerical simulation results of point target and distributed scene target are provided to demonstrate the effectiveness of the proposed approach.

The range ambiguous clutter in space-based early warning radar is more severe compared with that in airborne radar due to the faster movement velocity. Meanwhile, the clutter angle-Doppler characteristics vary with range owing to Earth’s rotation. As a result, the mainbeam clutter returned from different ambiguous range occupies most of the Doppler spectrum, which leads to the degradation of the traditional space-time adaptive processing (STAP) performance in canceling clutter. Though frequency diverse array (FDA) based on multiple-input multiple-output can provide the ability to identify the range ambiguous clutter, it cannot perform well for space-based early warning radar because of the finite available vertical elements. In this paper, a novel STAP method, which utilizes the element-pulse coding (EPC) and FDA technique to mitigate the range ambiguous clutter, is proposed. Firstly, EPC is used to pre-whiten the most range ambiguous clutter via coding and decoding in horizontal elements and coherent pulses. Then the interval in the vertical frequency of the residual range-ambiguous clutter is expanded by FDA, and thus the targets and clutter from the vertical mainlobe are easily extracted using a spatial filter with a few vertical elements. Finally, the extracted clutter can be effectively suppressed through the traditional STAP method. Simulation results are provided to demonstrate the performance of the proposed method.

This letter focuses on discriminating true target from mainlobe jammers using a frequency diverse array-multiple-input multiple-output (FDA-MIMO) radar. To this end, a three-stage detection and discrimination architecture is proposed. Specifically, mainlobe jammers caused by large time delays can be discriminated from the true target based on the range degrees-of-freedom (DOFs) of FDA-MIMO radar, while the true target and jammers generated with small time delays are distinguishable in the fast time domain. In particular, a binary hypothesis test is formulated for discrimination, where the$H_{1}$hypothesis contains the true target, while the$H_{0}$hypothesis contains the unknown mainlobe jammer. The mainlobe jammer can be formulated as either a known subspace model or an unknown rank-one model. The former leads to a subspace-based selective detector (SSD) and the latter generates a rank-one-based SD (ROSD). Then, the formulated tests are handled using the generalized likelihood ratio test (GLRT) criterion. At the analysis stage, the selectivity and detection performance are compared with the conventional GLRT and selective receivers. Simulation results validate that the proposed detectors achieve satisfactory detection performance while providing the selectivity to mainlobe jammers.

In this letter, the problem of adaptive target detection for a frequency diverse array-multiple-input multiple-output (FDA-MIMO) radar in the presence of unknown mutual coupling (MC) is investigated. Unlike the conventional phased array (PA) radar, the signal model of FDA-MIMO radar in unknown MC is constructed using two symmetric Toeplitz matrices accounting for the effects of unknown MC at the transmitter and receiver. At the formulation stage, the detection problem is formulated as either a binary hypothesis test or a multi-hypothesis test. The former is tackled based on the generalized likelihood ratio test (GLRT) leading to a robust detector (RD). The latter resorts to the penalized test and multifamily LRT (MFLRT), resulting to Modified-Multiple Hypothesis Penalized Likelihood Ratio Test (M-MHPLRT) and MFLRT detectors, respectively. At the analysis stage, the effectiveness of the developed detectors is verified with comparisons among the benchmarks and existing detectors via Monte Carlo simulation results

For the high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system, the increasing imaging width results in a serious range ambiguity problem, which affects the performance of ground moving target indication (GMTI). In this article, a novel nonuniform subpulse coding (NSPC) scheme is proposed. It is characterized by resorting to range-frequency band resources and detailed coding design for each subpulse, enabling the beam auto-scanning in elevation. Also, the bandpass filtering and digital beamforming (DBF) technology with improved data reconstruction are utilized to realize the separation of subpulses and suppress range ambiguity. The NSPC technique exchanges the signal bandwidth for increasing swath without range ambiguity, and the coded subpulses can be directed to the prescribed regions, while skipping the invalid areas where the echoes are blocked. After that, through the robust principal component analysis (RPCA) method, the moving target detection is performed for each separated region without residual range-ambiguous interference. The proposed approach has been theoretically deduced in detail and the simulation experiments demonstrate its effectiveness.

Forward-looking mode for moving target detection is important for airborne radar systems, however, it is difficult to suppress the range-dependent clutter in the presence of range ambiguity using the traditional space-time adaptive processing (STAP) techniques. In this paper, a vertical coherent frequency diverse array (FDA) radar using quadratic phase coding (QPC) is proposed to alleviate the range-ambiguous clutter problem. The proposed vertical coherent FDA radar has two major advantages: i) it uses an identical baseband waveform for each transmit element, which prevents the unreal orthogonal waveform assumption in multiple-input multiple-output (MIMO) framework; ii) it achieves wide spatial coverage in elevation within a single pulse duration, which is desirable for the airborne reconnaissance radar. The space-frequency coupled characteristic of vertical coherent FDA is revealed and a series of 2-dimensional (2-D) matched filters are designed, which is helpful for range-ambiguous clutter separation. Based on the QPC technique, the residual clutter is suppressed by orthogonal projection (OP) filtering, which further improves the target detection performance. With the proposed vertical coherent FDA using QPC, the range-ambiguous clutter can be separated successfully, and the clutter suppression performance is improved. Simulation results are presented to verify the effectiveness of the proposed method in serious range-ambiguous clutter scenarios.

This letter deals with the issue of clutter suppression with the helicopter-borne single-channel rotating synthetic aperture radar (SCRoSAR). Commonly, obtaining an synthetic aperture radar (SAR) image and implementing the ground moving target indication (GMTI) simultaneously is a challenging task for the single-channel SAR (SCSAR), since, the clutter background covers most of the slow-moving targets in the SAR image. In this letter, we propose a novel clutter suppression method via the time-Doppler chirp varying (TDCV) approach for the SCRoSAR system. A traditional RoSAR imaging algorithm and a modified RoSAR-TDCV imaging algorithm are employed to generate an original image and a TDCV image from the same set of data, respectively. Benefits from the characteristics of the TDCV approach, the position of moving targets will be shifting along the range direction in the TDCV image. Meanwhile, the position displacement of stationary clutter scatterers is neglectable. Therefore, via image cancellation, the clutter background can be significantly suppressed. With the proposed approach, the RoSAR system is capable of imaging the stationary objects and revealing the moving targets, simultaneously. The experimental results with simulated data demonstrate the validation of our proposed method.

As a new imaging framework, the rotating synthetic aperture radar (ROSAR) derives a synthetic aperture through antenna rotation instead of traditional linear platform motion. The rotational synthetic aperture is sensitive to high-frequency vibrations aboard helicopters. The vibration causes severe phase error in signal echoes and imaging degradation. Unlike traditional synthetic aperture radar (SAR) imaging where range-Doppler algorithm (RDA) maybe applied for autofocus to improve the imaging performance, vibration phase errors cannot be estimated via conventional imaging algorithms due to severe range and azimuth couplings. A new ROSAR imaging procedure is proposed to compensate the phase error resulting from high-frequency vibrations of helicopters. A vibration model of ROSAR signal echo is established. The double Doppler keystone transform (DDKT) is adopted to correct the range cell migration (RCM) induced by slant range history and vibration errors. Analytical echo expression with range-independent vibration phase error is also derived in greater details. The focused image can then be obtained via classical autofocus algorithms. The effectiveness of the proposed technique is sufficiently demonstrated through simulation studies.

In this letter, a novel method based on multifrequency pulse diversity (MFPD) is proposed to achieve high-resolution and wide-swath (HRWS) imaging by utilizing the displaced phase center antenna (DPCA) technique. In the MFPD mode, multiple waveforms from different frequency bands are transmitted through a single channel. Thus, within the same receive window, the echoes from different range regions correspond to different frequency bands, making it possible to separate the range ambiguous echoes in the range frequency domain. However, the azimuth sampling rate will be reduced in the MFPD mode, leading to the Doppler ambiguity. To this end, the MFPD-DPCA technique is utilized, which is capable of separating the range ambiguous echoes without loss of azimuth sampling rate. Moreover, the MFPD-DPCA technique can achieve high range resolution by spectrum splicing, which enhances the feasibility of super-high-resolution imaging. Finally, the HRWS imaging can be obtained by performing the traditional synthetic aperture radar (SAR) algorithm on the reconstructed unambiguous wideband echoes. The proposed method offers an alternative in system implementation but does not necessarily offer improved swath width over current classical HRWS-SAR methods. Numerical results corroborate the effectiveness of the considered HRWS imaging strategies in ambiguous scenarios.

It is hard to tackle the near-range clutter when range ambiguity exists in the non-sidelooking moving target detection (MTD) application. The angle-Doppler spectra of the far- and near-range clutter cannot be aligned simultaneously in this case due to the range dependence, which would significantly degrade the performance of the space–time adaptive processing (STAP) technology. To address this problem, an elevation element multifrequency subpulse coding (EMFSPC) array framework is proposed in this article. For each pulse duration, the main-lobe beam of the proposed framework can automatically sweep the full space by coding the subpulses and the transmitting elements, which steers the subpulses to different directions. Besides, these multiple subpulses occupy different range-frequency bands, and thus corresponding bandpass filters could be carefully designed to extract the unambiguous signals. After that, one can align the clutter spectra centers and employ the full-dimensional or dimension-reduced STAP techniques to achieve clutter cancellation. Furthermore, the simulation experiments are conducted to demonstrate the validity of the proposed EMFSPC system in near-range strong clutter suppression.

By transmitting a single frequency‐shifted waveform, coherent frequency diverse array (FDA) can provide a simple way to realize full spatial coverages with stable gains. Owing to the range‐angle dependency in coherent FDA, the authors implement a two‐dimensional angle‐time matched filter to perform equivalent transmit beamforming and matched filtering simultaneously. However, such filter structures merely control main‐lobes of equivalent transmit beams towards target directions. It fails to form nulls at interference directions. Moreover, traditional adaptive beamforming by designing adaptive weight vectors are no longer applicable. Additionally, they find that the range resolution scales linearly with the element number. To tackle these issues, a space‐time matched filter (STMF) in combination with the hybrid coding technique is proposed. Aiming at mitigating interferences, the STMF is designed with a formulation of quadratically constrained quadratic programing. By relaxation of quadratic constraints, the hard non‐convex problem can be turned into the second‐order cone programing to obtain optimal filter parameters. Furthermore, the hybrid coding technique is devised to jointly improve the range resolution. Numerical experiments of both two‐dimensional range‐angle outputs and one‐dimensional range profiles via filtering are provided, which demonstrate that the STMF with hybrid coding can effectively suppress sidelobe interferences with a range resolution enhancement.

Coherent frequency diverse array (FDA) can provide the full spatial illumination with a stable transmit gain by employing a single frequency‐shifted waveform. However, the authors find that the range resolution is scaled linearly with the number of elements. In this work, the problem is first quantitatively analysed through mathematical derivation. To solve the issue, a subarray‐based coherent FDA transmitting pulsed linear frequency modulation signals is proposed. The essence of the subaperture technique is to partition the transmit antenna array into multiple regular or irregular subarrays, wherein an identical carrier frequency is utilised in each subarray. Meanwhile, distinct carrier frequencies are adopted between subarrays. Moreover, the multi‐dimensional ambiguity function is studied to assess the properties including the range resolution, spatial coverage and sidelobe level. Simulation results demonstrate that the proposed approach has superiorities in range resolution enhancement and range sidelobe reduction.

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.

Beamforming plays a crucial role in synthetic aperture radar (SAR) for interference mitigation and ambiguity unaliasing. In this article, a series of novel beamforming methods for SAR systems is proposed based on nulling the transceive beampattern accurately in frequency diverse array (FDA)-multiple-input and multiple-output (MIMO) scheme. In general, these methods are implemented by assigning artificial interferences with prescribed powers within the given rectangular regions in the joint transmit-receive spatial frequency domain. In specific, according to the predefined null depths, closed-form expressions of artificial interference powers are first formulated. Then, iteration algorithms are developed to update the interference-plus-noise covariance matrix and the designed weight vector. In such a way, a trough-like transceive beampattern with arbitrarily distributed broadened nulls is formed in the joint transmit-receive spatial frequency domain. As a result, interferences mixed in signals received by SAR can be suppressed effectively. Numerical simulations and experimental results are provided to corroborate the effectiveness of the proposed methods.

Conventionally, near‐field scattering effect was considered in antenna measurement, which requires a great amount of measuring efforts and re‐measurement if the operational platform has changed. In this study, the authors consider the near‐field scattering problem in the framework of adaptive array beamforming theory, which avoids re‐measurement and can be adaptive to the arbitrary scenario. Generally, the near‐field scattering can result in severe performance degradation in adaptive beamforming applications. They propose a robust adaptive beamforming approach that can maintain the performance in the presence of near‐field scatterers with unknown scattering coefficients. In the authors’ approach, the near‐field scatterering signal component is incorporated into the presumed steering vector. Thus, it is treated as useful information in this study instead of nuisance interference in the literature. In particular, the properties of the far‐field direct‐path signal and near‐field signal are explored and the large uncertainty set is divided into two small ones describing the far‐field and near‐field steering vectors, respectively. Simulation examples are provided to show the performance improvement by making use of the near‐field scattering signals.

The clutter suppression and target radial velocity estimation are essential in the ground moving target indication processing with multichannel synthetic aperture radar (SAR) systems. In reality, the heterogeneous clutter, the image coregistration error, and channel mismatch will remarkably decline the estimation performance of the target radial velocity. To address these issues, a robust radial velocity estimation algorithm is proposed in this letter. Based on the joint-pixel signal model, the joint-pixel normalized sample covariance matrix (JPNSCM) is employed to mitigate the effect of heterogeneous clutter, and the shift vector determined by JPNSCM is used to obtain the actual target steering vector. Then, the adaptive matched filtering algorithm is adopted to estimate the target radial velocity. Compared with traditional estimation algorithms, the proposed method obtains better performance in both simulations and real SAR data experiments.

A rotating synthetic aperture radar (RoSAR) can image the environment in 360° on a stationary platform because it uses rotating antennas. The range-variant distortion in azimuth is evident in the RoSAR system, which degrades the imaging quality, particularly in high-resolution situations. In this letter, a new chirp z transform (CZT) imaging algorithm is developed to remove the range-variant distortion in azimuth for helicopter-borne RoSAR systems. Based on a fourth-order approximation of the slant range model, a precise expression of the 2-D spectrum of the echo is derived via the series reversion method. The spatial-variant characteristics of the range cell migration term and the second range compression term are analyzed and compensated. Then, according to the analysis on the range-dependent output sample spacing variation in azimuth, which is caused by the RoSAR configuration, an efficient CZT is proposed to remove the range-variant distortion in azimuth. The experimental results with simulated data are provided to clearly demonstrate the proposed approach.

Helicopter‐borne rotating synthetic aperture radar (ROSAR) enjoys fast imaging property as the synthetic aperture obtained by rotor rotation. However, ROSAR data processing is usually a challenging task due to severe range cell migration (RCM) and motion error. To solve this problem, an enhanced phase gradient autofocus (PGA) algorithm based on helicopter‐borne ROSAR wavenumber‐domain imaging approach is proposed in this study, which alleviates the imaging performance degradation due to the RCM and motion error. With even small motion error induced in the wavenumber domain, the influence of motion error will become evident after performing Stolt interpolation, which induces serious image defocusing and degradation. Hence, the authors further propose the PGA‐based ROSAR motion compensation scheme, which combines quadratic term correction with phase gradient estimation, and well‐focused ROSAR images can be obtained via iterative processing. Several results of simulated experiments are presented to validate the proposed method for helicopter‐borne ROSAR imaging.

The synthetic aperture radar (SAR), as a wideband radar system, operates over a large frequency band ranging from the low very high frequency to millimeter waves. It often overlaps in frequency with other radio-frequency systems including radio, television, and cellular networks. Therefore, radio-frequency interference (RFI) suppression is the severe test for SAR systems. Recently, some methods are proposed to suppress the RFI based on the sparse recovery in range-frequency domain and low-rank extraction in the azimuth dimension. However, all the previous methods exploit one property of the RFI, which may leave the room for performance improvement. Hence, in this paper, we propose three methods to jointly exploit the sparsity and low-rank property of RFI. We first include the sparse term and low-rank term of the RFI in the objective function to separate the RFI and the useful signal, which is defined as the joint sparsity and low-rank property method. It has better performance but heavier computational burden than the algorithm employing only the low-rank property. Then, we use row-sparse (RS) concept in lieu of the two properties, since the narrowband RFI has relatively stable frequencies during the synthetic aperture time. The RS method avoids the low-rank optimization and dramatically decrease the computational burden. Also for the real radar system, the sampling frequency is commonly larger than the frequency bandwidth. Therefore, there are some redundant data in the received signal. We exploit both the downsampling operation and the RS concept to speed up the convergence, which is called the fast row-sparse (FRS) method. The FRS method can further eliminate the out-of-mainband RFI. The real SAR data are provided to demonstrate the effectiveness of the three proposed methods.

Synthetic aperture radar (SAR), as a wideband radar system, is easy to be interfered by radio frequency systems, such as the radio, television, and cellular works. Since the narrowband radio frequency interference (RFI) has a relatively fixed frequency during the synthetic aperture time, it is removed as a low-rank term of the received signal in recent research. In this paper, we employ a novel “low-rank + sparse” decomposition model to extract the low-rank RFI and protect the strong scatterers of a useful signal, which is explicit and more efficient than the previous augmented Lagrange function model. Because the radar signal is complex, we exploit soft thresholding instead of hard thresholding in the Go Decomposition algorithm, which is defined as the revised traditional decomposition (RTD) method. Soft thresholding can recover the phase term correctly for a further focused image. Both the previous augmented Lagrange method and the proposed RTD method need to search the values of user parameters with high computational complexity. In order to eliminate the bother of tuning user parameters, a parameter-free decomposition (PFD) method is proposed to adaptively estimate the user parameters. Also, by considering the property of the useful signal, the PFD method protects the useful signal with adaptive thresholds for each snapshot. It has a better performance for RFI suppression, but costs slightly more computational time compared with the RTD method. The real SAR data and the measured RFI are provided to demonstrate the correctness of the proposed methods.

Multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has drawn considerable attention because of its extra degrees of freedom for high resolution and wide swath compared with the traditional multichannel SAR system. But how to extract the matched signal without the unmatched interferences is the foremost task for MIMO SAR system. In this paper, by using the orthogonal frequency division multiplexing chirp signals as the transmitted signals, it is demonstrated that the robust principal component analysis (RPCA) method can be successfully employed for ground moving target indication (GMTI) with no need for separating the matched signal and unmatched interferences. It is because the unmatched interference is proven to have low-rank property and noise-level magnitude, which can be separated apart from the matched signal with the RPCA method. However, the traditional RPCA methods may be restricted by the high computational burden due to the complex decompositions and multiple iterations. Hence, a fast interferometry RPCA method is proposed specially for GMTI mode, which takes full advantage of the characteristics of along-track interferometry SAR system. It can improve the probability of detection under low signal-to-clutter-and-noise ratio. Additionally, it will dramatically shorten the computational time. Furthermore, the proposed method can also estimate the radial velocities of the moving targets simultaneously. The results by applying the proposed method into a set of real SAR data are consistent with the analysis presented in this paper.

The conflict between range and azimuth ambiguities is a challenging problem for spaceborne high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) imaging. In this letter, a novel ambiguity resolution approach based on frequency diverse array (FDA) is proposed to retrieve unambiguous signal from that with ambiguities in both range and azimuth domains. By exploiting the range-angle-dependent property of transmit steering vector in FDA and applying the second range dependence compensation approach, echoes from different ambiguous regions are separable in transmit spatial-Doppler frequency domains. Then a corresponding transmit beamformer is designed to extract spectrum component of each ambiguous region, which can be rearranged to comprise the desired unambiguous signal. Compatible with most existing azimuth ambiguity suppression algorithms which employ the receive degrees of freedom, the proposed approach can further enhance the capability of resolving ambiguity for HRWS SAR systems, and its effectiveness is verified by simulation experiments.

Conventionally, a single-channel synthetic aperture radar (SC-SAR) system can hardly detect weak moving targets simply. In this paper, a time-Doppler chirp-varying (TDCV) filter is proposed for ground moving target indication and parameter estimation with the airborne SC-SAR system. The proposed method is easy to implement and mainly includes three steps. First, a traditional 2-D frequency range-Doppler algorithm is used to generate an original image. Second, the second-order range cell migration (RCM) phase term is partly compensated in the range frequency and azimuth time domain, and the rest of second-order RCM phase term is compensated in 2-D frequency domain. The whole processing, which is referred to as the TDCV approach, is employed to acquire a new TDCV image. Third, compared the original image with the new image, the clutter scatterers are nearly motionless while the moving targets are translated along the range direction due to their nonzero radial velocities. After the cancellation between two normalized images, the clutter background would be significantly suppressed since the two images generated by the same data. As a result, the moving targets can be indicated and the range difference of the moving target between two images can be exploited to estimate their radial velocities. The results obtained by applying the proposed method into a set of real SAR data are consistent with the analysis presented in this paper.

In this paper, a novel method for ground moving target indication (GMTI) and parameter estimation is proposed with single channel of synthetic aperture radar (SAR) system. The proposed method uses the varying Doppler chirp rate to get a new image, where the clutter scatters are motionless and the moving targets are translational along slant range direction compared with the original image. As a result, the moving targets can be indicated and the radial velocities of moving targets can be estimated by the offset of range cells. Numerical examples are illustrated to demonstrate the correctness of the proposed method.

Clutter suppression and ground moving target indication (GMTI) are challenging tasks for multichannel synthetic aperture radar (MC-SAR) systems. In recent years, robust principal component analysis (RPCA), such as the augmented Lagrange multiplier method (ALM) and go decomposition (GoDec) algorithm, has drawn considerable attention for its excellent performance in distinguishing the different parts from a set of correlative database. In this letter, an efficient along-track interferometry GoDec (ATI-GoDec) approach is proposed for GMTI in MC-SAR systems under a strong clutter background. The proposed method can be separated into two sections: the predetection and the postdetection. The predetection by an efficient ATI RPCA method can decrease missing targets, and postdetection with a novel magnitude and phase (M&P) detection has the ability to reduce the false targets. As a result, the proposed method can provide a more robust performance by a comparison to the traditional ATI detection. It can also widen the tolerant values of the preset cardinality and decrease the probability of false alarm compared with the conventional GoDec algorithm. Moreover, the proposed method only takes several iterations to reach the convergence by solving the optimization problem of the RPCA model, which makes it more efficient than the previous RPCA methods. The results by applying the proposed method into a set of real SAR data are consistent with the analysis presented in this letter.

A high-pulse-repetition-frequency (PRF) radar can handle the high Doppler frequencies of clutter echoes received by a fast-moving airborne radar. However, high-PRF radar causes range ambiguity. In addition, the clutter is range dependent when the airborne radar works in a forward-looking geometry. The range ambiguity and range dependence will lead to severe performance degradation of the traditional space-time adaptive processing (STAP) methods. In this paper, a vertical frequency diverse array (FDA), which applies frequency diversity in the vertical of a planar array, is explored to circumvent the range ambiguity problem in STAP radar. A range-ambiguous clutter suppression approach is devised, which consists of vertical spatial frequency compensation and pre-STAP filtering. In the vertical-FDA radar, the vertical spatial frequency depends not only on the depression angle but also on the slant range. By using this characteristic, the range-ambiguous clutter can be separated in the vertical spatial frequency domain, and then, clutter suppression is achieved for each separated range region. As a result, both problems of range ambiguity and range dependence are solved. Simulation results are provided to demonstrate the effectiveness of the proposed method.

Efficient motion parameter estimation is a key challenge for moving-target imaging and localization in the synthetic aperture radar ground moving-target indication system. However, the existing methods suffer from ambiguities, complex realization, or heavy computation load of O(MN). To solve these problems, we propose an efficient Radon transform (RT) and an efficient fractional Fourier transform (FRFT) to estimate the radial velocity and azimuth velocity. By exploiting the geometry information, we model a geometry relationship between the motion parameters and two transform angles of RT or FRFT. The matched motion parameters can be estimated by the geometry relationship of the mismatched results, and the computation complexity is reduced from O(MN) to O(2N) effectively. Additionally, the symmetry property can be used for clutter canceling. Simulated and experimental results demonstrate the validity of the proposed methods. Compared with conventional motion parameter estimation methods, the proposed methods are much more efficient in acquiring accurate estimation results.

In this paper, a robust beamforming method is proposed. This method can be viewed as a LCMV beamformer with its response vector further optimized. To generate a better response vector, it is first established as a non-convex quadratically constrained quadratic programming problem, and then is transformed into a semidefinite programming problem which can be efficiently and exactly solved via semidefinite relaxation. This method outperforms the traditional LCMV beamformer with lower sidelobe and well-maintained mainbeam. Moreover, the computation complexity is negligible because the size of the response vector is relatively small. Simulation examples are carried out to demonstrate the effectiveness of the proposed method.