Spaceborne distributed multi-transmit multi-receive (SD-MIMO) radar has great potential to enhance the capability of ground moving target indication (GMTI), particularly with the increasing of space-time-frequency degrees of freedom. However, GMTI performance is severely limited by complex geographical clutter. To address this issue, we proposed a clutter suppression method assisted by online classification using the range-Doppler-band 3D dataset of SD-MIMO radar. The core points and innovations of our method are summarized as follows. Firstly, we extend Markov random field (MRF) classification to 3D space by integrating a 3D adaptive weighted penalty (3D-AWP) function and a probability distribution model of multi-channel clutter. The 3D-AWP function is constructed using a novel spatial distance metric designed to characterize the interaction potential of image pixels within their 3D neighborhood. This ensures that the MRF smooth factor can be adaptively adjusted in both homogeneous and inhomogeneous clutter regions through different weighting values. The probability distribution model of clutter is established by the K-Wishart distribution, which incorporates amplitude and interferometric phase between multiple spatial channels. Then, under the Bayesian framework, we achieve classification of multiband SAR images by solving the maximum posterior probability problem. Subsequently, the homogeneous and inhomogeneous regions are identified based on classification results. And adaptive clutter suppression processing is performed independently for each sub-region. Finally, the SAR-GMTI experiments are performed utilizing synthetic data and measured data. The results demonstrated an improvement in clutter suppression capability by 2 dB and an enhancement in target oSCNR by 1.5 dB compared to traditional methods.

Distributed aperture coherence multi-transmit multi-receive (MIMO) radar combined with space time adaptive processing (STAP) has great advantage for moving target indication. However, the problems of non-ideal orthogonal waveform and non-stationary clutter are intertwined together, which leads to significant performance degradation of STAP. In this letter, we presented a pragmatic and accurate performance prediction model to interpret the impact mechanism of waveform properties on STAP performance. We deduced a novel clutter covariance matrix (CCM) model with the aforementioned non-ideal factors taking into consideration. On this basis, a closed-form expression of output SCNR loss is derived to predict and evaluate STAP performance. Therefrom the quantitative relationship is established between waveform properties, clutter CCM, and STAP performance. It provides a simple and effective way to predict system performance for radar system design. Finally, numerical simulation results illustrated that the mean prediction error of SCNR loss for proposed model is about 2dB less than conventional approximation model.

In the inshore region, the azimuth ambiguity of land clutter is the main factor, which reduces the clutter suppression performance for the multichannel synthetic aperture radar (SAR) system. To address this problem, we propose a multifeature tensor discriminant alternating optimization and affine invariant Riemannian (TDAO-AIR) classification approach. First, the correlation coefficient between channels is obtained to characterize the normalized SAR image amplitude. Next, multilook interferometric processing is performed to reduce the effect of Speckle noise and acquire multilook interferometric complex images. At the same time, we extract the correlation coefficient gradient and the multilook interferometric phase gradient in different spatial directions to characterize the spatial structure. Next, we construct a feature tensor (FT), including correlation coefficient between channels, multilook interferometric phase, and spatial structural gradient information for each range-Doppler unit (RDU). The TDAO algorithm is proposed to extract the core FT (CFT) by optimizing the objective function and getting the projection matrix. Then, the CFT is unfolded along the feature dimension, and the feature covariance matrix (FCM) is constructed for each RDU. With the AIR distance measure, the inshore region is divided into different clutter regions. Finally, in the azimuth ambiguous clutter region, independent and identically distributed (IID) training samples are selected to estimate the clutter covariance matrix (CCM), and robust adaptive clutter suppression is performed in the SAR image domain. The experiments on simulated and real measured data by TerraSAR-X demonstrate that the proposed method can accurately identify and suppress the azimuth ambiguity in the inshore region.

For a space-based radar system with hybrid baseline, the problem of clutter angle-Doppler spectral broadening poses a significant challenge to clutter cancellation in the terrain fluctuant observation scene. To improve the robustness of clutter suppression, this paper proposed a homogeneous sample selection method based on a novel concept of Generalized Spatial Spectrum Density Function (GSSDF). This method can be summarized as three crucial steps. First, the GSSDF was constructed by the prior information of DEM data, radar system parameters, and the backscattering model. Then, the angle of deflection (AOD) and the equivalent bandwidth (EBW) of GSSDF were adopted to measure the diffusion of clutter spectral. Subsequently, the sample selection criterion was established by a new threshold detection strategy. But the key here is that an appropriate detection threshold of the AOD and EBW can be determined by the characteristic of filter response. To summarize, this approach ensures that the training samples sharing similar clutter properties can be selected to estimate clutter covariance matrix (CCM); thereby, enhances clutter suppression capability. Finally, the experimental results demonstrated that the proposed method can obtain better clutter suppression performance than other contrast methods.

The purpose of this letter is to solve the space-time off-grid problem caused by the extremely inhomogeneous clutter spectrum under the space/airborne bistatic system. For the estimation of inhomogeneous clutter spectrum, the method of sparse recovery (SR) is usually adopted. However, performance of sparse recovery may degrade since the real clutter point often does not fall in the center of the space-time grid. To deal with this issue, a local mesh splitting algorithm is proposed in this letter. First, select the space-time vector atoms which are most relevant to the residuals by subspace projection. Then, iterative local mesh splitting is performed around the selected atoms to match the real clutter points. Finally, the clutter subspace is reconstructed from all matched atoms selected by global and local iterations. The simulation results show that the method can obtain better accuracy with lower computational effort.

Synthetic aperture radar (SAR) operating in an along-track interferometric (ATI) mode has the advantage of minimum detectable velocity (MDV) in active marine target detection. However, most of the conventional ATI detectors fail to identify the marine targets cruising with blind speeds, in which case the interferometric phases of these targets are closely distributed around those of the sea background due to phase wrapping and aliasing through ATI processing. To address this issue, we propose a method to detect the active marine targets based on complex interferometric dissimilarity for dual-channel ATI-SAR systems. First, an interferometric bilateral filter is designed to smooth the random noises and locally adapt to the spatial structure of the interferogram for measuring the interferometric magnitude and phase. Then, the target detection metric is constructed based on the complex interferometric dissimilarity between the marine targets and the sea background. By adaptively regressing into a magnitude-based test toward the blind-speed targets, the target detection metric can mitigate the blind-speed detection problem and thus yield a satisfactory detection result. Furthermore, this metric is of a constant false-alarm rate (CFAR), and its probability density function (pdf) is derived to facilitate the detection threshold computation. Finally, both the simulated and real-data processing results are given to validate the superiorities of the proposed method.

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.

Space–frequency modulation (SFM) signal is a potential waveform for multifunction radar with high degrees of freedom of space, time, and frequency. However, the introduction of the additional communication function will modulate the transmitting signal, which will severely deteriorate the autocorrelation function (ACF). Here, a modified CLEAN method is proposed to eliminate the influence of undesired sidelobes in ACF on the air target detection. In the proposed method, undesired sidelobes are treated as extra features of real target to obtain more precise estimation of its complex reflection coefficient. Moreover, by considering about the sparsity of air targets, the authors employ the sparse representation method to estimate the response of the current strongest target. Then the target occlusion is eliminated by iterative cancellation. Simulation results demonstrate that the proposed detector is reliable and effective for the SFM‐based integrated system.

In this paper, we propose a long-time coherent integration method for a maneuvering target with the first-, second-, and third-order range migrations, and the complex Doppler frequency migration. In this method, after range compression, the echo signal is first transformed into the range-frequency and Doppler domain based on series reversion, and then an azimuth matched filtering procedure is implemented in the 2-D frequency domain. It can eliminate the coupling effects between range and azimuth jointly caused by the radial velocity, radial acceleration, and radial acceleration rate of a moving target. Due to the linear transform property, the proposed method can work well under low signal-to-clutter and noise ratio. In addition, the Doppler ambiguities, when target azimuth spectrum either is within a pulse repetition frequency (PRF) or spans over neighboring PRF bands, can be well solved. Both simulated and real synthetic aperture radar data processing results are provided to validate the effectiveness of the proposed algorithm.

Geosynchronous orbit synthetic aperture radar (GEO SAR) plays an important role in wide-area surveillance and the continuous coverage of areas containing targets of interest. However, the relative position errors of large aperture arrays will distort the antenna pattern, which significantly degrades the target detection performance in a GEO SAR system. To address this issue, most of the conventional calibration methods are focused on the independent errors, without consideration of the parametric error model, which may increase the position estimation errors. To solve this problem, an efficient calibration algorithm for position errors in a GEO SAR is proposed in this letter. For the large antenna arrays, the parametric error model is first established. Then, the calibration method is performed to estimate the parameters of the position error model. Based on this, the accurate position errors can be obtained by the least-squares algorithm. Compared with the conventional methods, the target detection performance of a GEO SAR system can be significantly improved after the precise array position error compensation by the proposed algorithm. Moreover, the proposed algorithm transforms the estimation from 3-D positions to the finite parameters of the error model, which can considerably decrease the computational complexity and obtain a more accurate estimation of the position errors simultaneously. Several simulated results are presented to validate the proposed algorithm for the position error correction in a GEO SAR system.

In this paper, a new method is proposed to refocus a ground moving target in synthetic aperture radar imagery. In this method, range migration is compensated in the 2-D frequency domain, which can easily be implemented by using the complex multiplications, the fast Fourier transform (FFT), and the inverse FFT operations. Then, the received target signal in a range gate is characterized as a quadratic frequency-modulated (QFM) signal. Finally, a novel parameter estimation method, i.e., scaled generalized high-order ambiguity function (HAF), is proposed to transform the target signal into a signal on 2-D time-frequency plane and realize the 2-D coherent integration, where the peak position accurately determines the second- and third-order parameters of a QFM signal. Compared with our previously proposed generalized Hough-HAF method, the proposed method can obtain a better target focusing performance, since it can eliminate the incoherent operations in both range and azimuth directions. In addition, the proposed method is computationally efficient, since it is free of searching in the whole target focusing procedure. Both simulated and real data processing results are provided to validate the effectiveness of the proposed algorithm.

This paper develops a framework based on enhanced shadow-aided decision for multichannel synthetic aperture radar-based ground moving target indication system according to the relationships between the moving target and its shadow information in position, dimensions, and intensity. As a sort of feature information available, the moving target shadow may improve the ground target detection performance. A critical precondition for shadow utilization is to obtain the good detection performance for the moving target shadow. However, shadow detection performance will deteriorate inevitably as a result of target motion that blurs its shadow. To address this issue, a knowledge-aided shadow detection algorithm with adaptive thresholds is proposed to improve the shadow detection performance in the developed framework. Furthermore, the theoretical performance analysis is performed, which indicates that the proposed knowledge-aided shadow detection algorithm has a better performance than that of the conventional shadow detection algorithm with a fixed threshold. Finally, numerical simulation experiments are presented to demonstrate that the developed framework can obtain good results for extended ground moving target detection.

This paper proposes a new method to focus a ground moving target with complex motions and estimate its motion parameters in a synthetic aperture radar (SAR) system. In this method, the second-order Keystone transform is applied to correct the range curvature. Then, the Hough transform is applied to estimate the slope of the range walk trajectory, from which the target cross-track velocity is obtained. Finally, a generalized Hough-high-order ambiguity function (GHHAF) transform is applied to transform the target signal into a 2-D time-frequency plane and estimate its slope associated with the third-order Doppler parameter. Compared with the conventional SAR imaging methods using the second-order phase model, the proposed method can obtain better imaging quality since the third-order Doppler frequency migration is effectively eliminated. Both simulated and real data processing results are provided to validate the effectiveness of the proposed algorithm.

In synthetic aperture radar (SAR), long integration time may induce range migration and Doppler frequency migration of a received signal, which may degrade the SAR imaging performance of ground moving targets. Most of the conventional algorithms deal with the problems of range migration and Doppler frequency migration based on parameter searching. However, the exhaustive searching of target motion parameters may result in heavy computational burden. To avoid this problem, this paper proposes a new imaging method for ground moving targets without target motion parameter estimation. First, Keystone transform is applied to correct the range walk. Second, range curvature is compensated by the matched filtering function. Third, Doppler frequency migration is compensated via multiplying the data in range- and azimuth-time domains by its reversed conjugate data according to the equal interval sampling of the azimuth slow time, which avoids the searching procedure for target motion parameter estimation. Finally, the signal energy will be well accumulated in the range-Doppler domain, and thus, the moving targets can be efficiently recognized in the focused image. The major advantage of the proposed method is that it can obtain well-focused images of all targets in one processing step without target motion parameter estimation; thus, it is computationally efficient. Both simulated and real data processing results are used to validate the effectiveness of the proposed method.

For radar target detection, the clutter distribution model needs to be identified first. The goodness of fit (GoF) between the original data and the assumed distribution can be used to choose the proper distribution model. Generally, the GoF is obtained using data histogram and theoretical distribution curve, and then the distribution model is judged via GoF. However, when the sample number is small, the histogram is rough and fluctuating, affecting the analysis of GoF. For the small sample, the graphical characteristic is obtained with the sample data to choose the most fitting distribution to the data in this paper. The graphical characteristic is acquired by a simpler process, that is, the original data are directly set as the test statistics, avoiding computing and sorting of other statistics. In this paper, the real airborne circular synthetic aperture radar data under different scan angles are analyzed using the GoF corresponding to histogram and graphical GoF, respectively. The results show that when the sea-clutter data histogram is close to two distributions, a more fitting distribution model may not be obtained by traditional GoF, but can be acquired by graphical representation. In addition, the sea data with different sight angles have different match properties. It is seen that the sea data are closer to the Rayleigh distribution in side-looking mode than that in big squint-angle mode, while the Weibull distribution and K distribution show equal fitting performance to sea clutter under variant radar sight angles.

Adaptive ground moving target indication (GMTI) algorithms based on the sample matrix inversion require the availability of a secondary data (training data) set to determine the adaptive filter. A polarization-aided GMTI method is devised in this letter for selecting this training data, which could improve the detection performance in heterogeneous environments. In particular, improved classification results are first obtained with the proposed polarization-space 2-D Wishart classifier, which are then employed in a generalized inner product algorithm to select the secondary data. The proposed scheme is able to provide a better choice of secondary data, resulting in considerable improvement in detection performance. Numerical results are provided to show the effectiveness of the proposed method.

In this letter, a reduced-rank beamforming method is presented based on knowledge-aided joint iterative optimization. The proposed adaptive reduced-rank processing is realized by joint optimization for the reduced-dimension matrix and beamforming weight vector. Meanwhile, recursively updating the prior covariance matrix using the spatial spectrum reconstruction technology and the weighted processing improve the estimation precision of the array covariance matrix. The simulation results show that the proposed method is robust to the dimension of the reduced-dimension matrix and significantly improves the output signal-to-interference-plus-noise ratio (SINR) of the adaptive beamformer under the condition of few samples.

With the observation distance of the radar increasing, the multichannel high-resolution synthetic aperture radar system may suffer from the reduction of the target signal-to-noise ratio, which leads to degradation in the detection performance for ground moving target indication (GMTI). Fortunately, the shadow feature, apart from the amplitude and interferometric phase of a moving target, may be available to improve the performance for target detection. In this letter, according to the geometric relationships between the moving object and its shadow in position and size, a shadow-aided method for GMTI is proposed. In addition, an efficient shadow detection method based on multifeature fusion is discussed to improve the shadow detection performance. Finally, numerical simulation results show that the shadow-aided method has a better detection performance, compared with the traditional detection algorithms.

In synthetic aperture radar (SAR) imaging of a ground moving target, long-time coherent integration may effectively improve the imaging quality, whereas the imaging performance may severely degrade due to the range migration and the Doppler frequency migration. In this paper, a novel motion parameter estimation method named second-order Wigner-Ville distribution (SoWVD) transform is proposed, and then, a new SAR imaging method based on the SoWVD for a ground moving target is developed. As a modified Wigner-Ville distribution method, the SoWVD method can estimate the motion parameter without the search procedure, which achieves motion parameter estimation by Fourier transform operations in the 2-D frequency plane with respect to the slow time and the delay time. In addition, it can effectively recognize the cross terms based on multiple symmetrical properties of the peaks in the 2-D frequency domain. Both simulated and real data processing results are presented to validate the proposed imaging method.

In the conventional space-time signal model, the statistical amplitude of the clutter is assumed to be a specific distribution. For sea clutter, a pulse-to-pulse correlation matrix is added to the temporal covariance matrix to describe the motion of the sea surface. However, such sea clutter model cannot reflect the property of the sea clutter. In this paper, a space-time model for the sea clutter is presented based on the physical sea surface model, in which the clutter amplitude is deterministic instead of statistic. The reflectivity and the radial velocity of the sea clutter for any specific position and time are computed based on the physical sea surface. Both the aforementioned two factors vary with time, which corresponds to the time variation of the sea surface. Moreover, the spatial channel decorrelation is modeled, which has an effect on the spatial covariance matrix of the sea clutter. The simulated angle-Doppler spectra and the signal-to-clutter-plus-noise-ratio loss show that the reflectivity of the sea clutter makes the space-time-adaptive-processing performance degrade, and the radial velocity of the sea clutter results in a more significant spread of the clutter power spectrum in sea state 4 than sea state 2. The Doppler spectrum can be acquired by the space-time model proposed in this paper instead of the experimental Doppler model for sea clutter. For a given sea state, the low range resolution has a more prominent effect on the power spectrum because of severer spatial channel decorrelation.

In practice, the ambiguous image of the strong ground clutter may dominate in the adjacent water areas in the case of high-azimuth ambiguity level for the spaceborne synthetic aperture radar. In this letter, a robust ambiguous clutter suppression method is proposed for the ground moving target indication. The subspace projection approach is used to suppress the ambiguous clutter, and the notch broadening technique is used to widen the ambiguous clutter notches for the robustness. The width of the notch is determined by the probability density function of the interferogram's phase. The validity and robustness of the proposed method are verified by the simulation results.

In practice, inevitable image coregistration error and channel phase mismatch will significantly degrade the estimation performance of the target radial velocity in the ground moving target indication processing with multichannel synthetic aperture radar (SAR) systems. To overcome this problem, a new radial velocity estimation method using the subspace projection (SP) algorithm with adaptive data reconstruction is proposed in this letter. Based on the joint-pixel signal model, the Wiener weight vector is used to reconstruct the multichannel data vector of the pixel containing the moving target. Then, the SP algorithm is adopted to deal with the radial velocity estimation with the reconstructed single “snapshot” data. The validity and robustness are verified by both simulations and real SAR data experiments.

Using multichannel in azimuth to suppress the Doppler ambiguities allows for high-resolution wide-swath (HRWS) synthetic aperture radar (SAR) imaging, thus overcoming the minimum antenna area constraint of the conventional space-borne SAR. If the degrees of freedom in azimuth are used for the clutter suppression, the ground moving target indication (GMTI) can be achieved. Therefore, a space-borne multichannel SAR system for GMTI has the potential to offer HRWS imaging capability to some extent. However, here, the GMTI performance may suffer from the Doppler ambiguity caused by the undersampling. This paper focuses on the analysis of the influence of pulse repetition frequency (PRF) on GMTI performance in Doppler ambiguity. The multichannel signal models of the clutter and the moving target with Doppler ambiguity are derived in complex image domain. Considering the Doppler ambiguity and the multichannel spatial ambiguity, the influence of PRF on the output signal-to-clutter-plus-noise ratio after adaptive clutter suppression and the signal-to-noise ratio loss after SAR imaging is discussed. Accordingly, the design considerations of PRF for optimizing GMTI performance in multichannel SAR systems with HRWS imaging capability are given, along with a simulation example. Finally, the real-data experiments verify the theoretical investigations.

Under the condition of time variant RFI, the limitation of training sample size causes great performance degradation of the conventional Radio Frequency Interference (RFI) suppression algorithm based on eigen-subspace projection (ESP) method. A novel RFI suppression method using iterative adaptive approach (IAA) and orthogonal subspace projection (OSP) method is proposed for synthetic aperture radar (SAR). Dispensing with parametric search and model order estimation, the proposed method estimates the RFI power spectrum adaptively and iteratively, utilizing few training samples and filtering the RFI based on the OSP method. Both the simulation and experimental results are provided to illustrate the performance of the proposed method.

Phase unwrapping is the key problem in building the digital elevation model (DEM) of a scene from interferometric synthetic aperture radar (SAR) system data. In this paper, we propose a method of phase unwrapping based on the model of the multibaseline joint data group. The method can not only adaptively coregister the SAR images, but also accurately provide the accurate estimation of the terrain unwrapped phase in the presence of the large coregistration errors. Moreover, the improvement in computational complexity is achieved by using the multibaseline joint data group. The method is investigated by simulations, and results show successful phase unwrapping even if the image coregistration error is close to one pixel.

With multisatellite radar systems, several additional features are achieved: multistatic observation, interferometry, ground moving target indication (GMTI). In this letter, a new reduced-dimensional method based on joint pixels sum–difference$(\Sigma {-} \Delta)$data for clutter rejection and GMTI is proposed. The reduced-dimensional joint pixels$ \Sigma {-} \Delta$data are obtained by the orthogonal projection of the joint pixels data of different synthetic aperture radar (SAR) images generated by a multisatellite radar system. In the sense of statistic expectation, the joint pixels$\Sigma {-} \Delta$data contain the common and different information among SAR images. Then, the objective of clutter cancellation and GMTI can be achieved by adaptive processing. Simulation results demonstrate the effectiveness and robustness of the proposed method even with clutter fluctuation and image coregistration errors.