Ground Moving Target Indication (GMTI) has always been one of the key tasks in synthetic aperture radar (SAR) systems. For SAR-GMTI, existing GMTI algorithms can be classified into traditional signal processing algorithms and low-rank matrix recovery algorithms. Space-time adaptive processing (STAP), as a typical traditional approach, has shown excellent performance in detecting moving targets in real applications; low-rank matrix recovery algorithms have become one of the recent research hotspots. Robust principal component analysis (RPCA), as a typical low-rank matrix recovery approach, has been applied in SAR-GMTI due to its ability to decompose an approximate low-rank matrix into a low-rank component and a sparse component. However, a drawback of RPCA algorithms is its relatively high false alarm rate caused by the energy leakage from strong clutter. The detection performance of the STAP algorithm may degrade when the training samples are contaminated. To address these challenges, we propose a block-based RPCA-STAP algorithm that integrates traditional adaptive clutter suppression methods into the RPCA framework. The proposed algorithm first implements clutter classification using the Markov random field (MRF) image segmentation algorithm. Then, RPCA-STAP suppression is applied to different clutter blocks, which not only satisfies the IID requirement for STAP training samples but also reduces the false alarm rate caused by strong clutter in the RPCA algorithm by introducing STAP. Additionally, considering the sensitivity of GMTI algorithms to channel errors, we propose an improved adaptive 2-D calibration (IA2DC) algorithm to further enhance channel correlation. Simulation and real data experiments validate the effectiveness of the proposed algorithms.

Moving targets always defocus and shift outside the scene in video synthetic aperture radar (video SAR) image sequences. However, the shadows of moving targets are immune to these issues and can reveal the true position of the moving targets. As such, by tracking the shadows of moving targets in the video SAR image sequence, it becomes feasible to keep track of these targets. Nevertheless, due to the small pixel size and time-varying characteristics of the target shadow, current prevailing tracking methods often prove insufficient for direct tracking of the shadow. In this paper, a shadow-assisted tracking method for moving targets based on multilevel discriminant correlation filters network (MDCFnet) is proposed. Primarily, we designed a Reverse Feature Pyramid Network (RFPN) that integrates multiple high-level features into low-level features to obtain multiple features with higher distinguishability and resolution, thereby enhancing the final tracking accuracy and precision. Furthermore, we devised Multi-level Discriminative Correlation Filters (MDCF) to perform filtering tracking under multiple feature maps. Real dataset processing results are provided to demonstrate that the proposed method outperforms other state-of-the-art methods.

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.

In remote sensing research, narrowband interference (NBI) suppression in synthetic aperture radar (SAR) is an urgent problem. Recently, many methods on NBI suppression are proposed via sparse recovery. In these methods, the optimal regularization constants are always hard to choose. Moreover, the number of NBI signals, equaling to the sparsity of the sparse vector, may change at different pulses, and the suppression performance might be reduced due to the regularization constants which control the sparsity of the sparse vector, are fixed. In this letter, aiming at these problems, a NBI suppression method for SAR based on sparse segmentation search (SSS) is proposed. Firstly, we build a non-convex optimization model without the regularization constant. Then the adaptive linear enhancer (ALE) is used to convert the non-convex model to a convex one. Finally, we solve this convex model and suppress NBI signals. The real-world SAR data experiments illustrate the effectiveness of the proposed method.

Deceptive jamming suppression in synthetic aperture radar (SAR) is an urgent problem. In this letter, an auxiliary-channel slow-time coding and change detection based deceptive suppression method is proposed. By coding pulses from the auxiliary channel, difference between two images, separately from the auxiliary and main channel, is appeared. Then, the change detection technology is utilized to reveal this difference as well as to form filters and to suppress deceptive jammings. Compared to some existing coding methods, in this method, deceptive jammings can be suppressed more thoroughly and a higher quality SAR image can be achieved. Results of false scene based simulation experiments demonstrate the effectiveness of the 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.

The rank of the sparse signals brought by multiple measurement vectors (MMV) augments the performance of joint sparse recovery. In general, suppose the sparsity level k is less than or equal to [rank(X)+spark(A)-1]/2, the sparsest solution of the MMV problem is unique and recoverable via various methods. It is shown in this letter that the unique solution of the sparsity level k up to spark(A)-1 actually exists in a measure theoretical point of view. More specifically, even when [rank(X)+spark(A)-1]/2 ≤ kA), the sparsest solution toAX=Yis still unique with full Lebesgue measure in every k-sparse coordinate space. This phenomenon is fully confirmed by the MMV tail-l2,1minimization technique. Furthermore, the phenomenon that the traditionall2,1minimization actually fails to recoverXwith k ≥ [spark(A)-1]/2 is investigated from the same perspective of measure theory. Extensive numerical tests conducted by the MMV tail-l2,1minimization andl2,1minimization are demonstrated to confirm the findings. The tail minimization procedure exhibits the most prominent effectiveness for the larger sparsity levels among all known techniques.

Real-time radial velocity estimation is a key challenge for moving target imaging and location in current single-antenna synthetic aperture radar (SAR)-ground moving target indication systems. Since the conventional methods suffer from ambiguity, complexity realization, or heavy computation load for fast moving target motion estimation, this paper emphasizes the estimation efficiency by simple realization. An efficient Radon transform (RT) estimation is proposed to estimate the radial velocity of fast moving target by utilizing the geometry information, and much more geometry information is exploited to realize clutter cancellation, noise cancellation, and estimation error minimizing in the RT domain, which is not proposed by the others. With only two to four angles used to calculate rather than search for the radial velocity of moving targets, the proposed methods simplify the conventional range and angle (2-D) searching procedure into several time range (1-D) searching procedure efficiently. The theoretical and experimental analysis provides qualitative and quantitative evaluations into the effectiveness of the proposed methods. In the single-antenna SAR system, the proposed methods can estimate the radial velocity of fast moving target efficiently and accurately in high signal to clutter plus noise ratio scenarios.

In this paper, we consider the direction of arrival (DOA) estimation problem under the coexistence of noncircular and circular signals. By exploiting the difference between the circularity of noncircular and circular signals, a method is proposed, which estimates the DOAs of noncircular and circular signals separately. The maximum number of detectable directions by the proposed method is twice that by the MUSIC method. Furthermore, since the proposed method resolves noncircular and circular signals based on the circularity difference rather than the DOA difference, the proposed method performs well regardless of the DOA separation between noncircular and circular signals. Simulation results illustrate the effectiveness of the proposed method.

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.