Heterogeneous Clutter Suppression for Airborne Radar STAP Based on Matrix Manifolds

Chen, Xixi; Cheng, Yongqiang; Hao, Wu; Wang, Hongqiang

doi:10.3390/rs13163195

Cited by 10 publications

(5 citation statements)

References 39 publications

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“…Recently, related studies on radar target detection methods based on information geometry received a lot of attention for their distinct advantages in hetero-clutter environments [32][33][34][35][36][37][38][39][40][41][42][43][44]. Specifically, the matrix information geometry (MIG) detector, which was pioneered by Barbaresco [45] and developed by Cheng et al [46], shows a novel detection scheme for target detection.…”

Section: Introductionmentioning

confidence: 99%

A Steering-Vector-Based Matrix Information Geometry Method for Space–Time Adaptive Detection in Heterogeneous Environments

Zou,

Cheng,

et al. 2024

Remote Sensing

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Plagued by heterogeneous clutter, it is a serious challenge for airborne radars to detect low-altitude, weak targets. To overcome this problem, a novel matrix information geometry detector for airborne multi-channel radar is proposed in this paper. The proposed detector applies the given steering vector and array structure information to the matrix information geometry detection method so that it can be used for space–time adaptive detection. While improving the detection performance, the matrix information geometry detector’s original anti-clutter advantage is enhanced as well. The simulation experiment results indicate that the proposed detector has advantages in several of the properties related to space–time adaptive detection, while its computational complexity does not increase significantly. Moreover, experiment results based on the measured data verify the superior performance of the proposed method. Sea-detecting data-sharing-program data, mountaintop data, and phased-array radar data are employed to verify the performance advantage of the proposed method in heterogeneous clutter and the ability for weak target detection.

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Section: Introductionmentioning

confidence: 99%

A Steering-Vector-Based Matrix Information Geometry Method for Space–Time Adaptive Detection in Heterogeneous Environments

Zou,

Cheng,

et al. 2024

Remote Sensing

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“…In addition, as the use of high-dimensional radar systems with multiple pulses and large arrays increases the DOF, the required number of homogeneous samples also increases. Due to the reduction in the number of available high-quality samples, the CCM estimated by traditional statistical methods cannot well characterize the clutter of the CUT, which will result in a severe degradation of the STAP performance and affect the clutter suppression [9,10]. Therefore, it is of great practical significance to obtain high STAP performance with small sample support.…”

Section: Introductionmentioning

confidence: 99%

Estimation of clutter covariance matrix in stap based on knowledge-aided and geometric methods

Jing,

Du,

Tian

et al. 2023

Phys. Scr.

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The insufficient number of available samples can cause inaccurate estimation of the clutter covariance matrix (CCM) in space-time adaptive processing (STAP), resulting in degraded clutter suppression performance. To tackle this problem, a CCM estimation approach based on knowledge aided and geometric methods is proposed in this paper. A combination of environmental as well as structural (Persymmetric or Symmetric structure) knowledge information is utilized to model the covariance matrix of each sample as a knowledge-aided Hermitian positive definite (KAHPD) covariance matrix. The estimation problem is introduced into the Riemannian manifold composed of the KA-HPD covariance matrices, and the geometric method is used for nonlinear processing. Based on the Kullback-Leibler (KL) divergence and the KL mean, the final estimated CCM is designed as a weighted combination of each KA-HPD covariance matrix. Experiment results show that the two designed structural covariance matrix estimators possess superior clutter suppression performance.

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“…However, since airborne radar systems operate in a down-looking mode, the strong backscattering generated from ground clutter scatterers is superimposed, and the signal-to-clutter plus noise ratio (SCNR) may be extremely low for targets with low radar cross-section (RCS). Moreover, the Doppler spectrum of the clutter may be severely spread due to the fast motion of the platform, and slow-moving targets may be totally submerged among different Doppler channels [4]. Fortunately, compared with the inherent limitations of traditional single-channel systems, radar systems equipped with multiple channels along the track direction have more degrees of freedom (DOFs) to suppress clutter and jamming, and they are more convenient to characterize the correlated properties between data in different channels.…”

Section: Introductionmentioning

confidence: 99%

Ground Moving Target Detection and Estimation for Airborne Multichannel Radar Based on Coherent Difference Processing

et al. 2022

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Ground moving targets with slow velocity and low radar cross−section (RCS) are usually embedded in the clutter Doppler spectrum. To achieve the detection and estimation of such targets, a novel method operating in the range−Doppler domain is developed for airborne multichannel radar systems. The interferometric phases that are sensitive to moving targets are obtained by coherent difference processing (CDP) for target detection. Moreover, the amplitude is utilized as complementary information to improve the detection performance. Then, a matched filter bank is designed and applied to the CDP processed data to complete the parameter estimation. The proposed method provides the benefits of high efficiency and robustness, since it does not involve matrix inversion, and it does not require homogeneous clutter assumption unlike adaptive algorithms. Experiments on real data acquired by an airborne X−band four−channel radar system demonstrate its effectiveness.

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Heterogeneous Clutter Suppression for Airborne Radar STAP Based on Matrix Manifolds

Cited by 10 publications

References 39 publications

A Steering-Vector-Based Matrix Information Geometry Method for Space–Time Adaptive Detection in Heterogeneous Environments

A Steering-Vector-Based Matrix Information Geometry Method for Space–Time Adaptive Detection in Heterogeneous Environments

Estimation of clutter covariance matrix in stap based on knowledge-aided and geometric methods

Ground Moving Target Detection and Estimation for Airborne Multichannel Radar Based on Coherent Difference Processing

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