A Grid-Less Total Variation Minimization-Based Space-Time Adaptive Processing for Airborne Radar

Su, Yunfeng; Wang, Tong; Li, Zhongyue

doi:10.1109/access.2020.2972366

Cited by 11 publications

(3 citation statements)

References 31 publications

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“…Different from the discretized CS scheme, ANM assumes frequencies distributed in an infinite dictionary and can recover sparse signals precisely in a continuous space. Because of its good performance, the ANM algorithm has been applied to sparse SAR imaging [16], DOA [17], space-time adaptive processing (STAP) [18,19], and downward-looking sparse linear array three-dimensional SAR imaging [20]. In this paper, we propose a TomoSAR imaging algorithm based on ANM, referred to as Tomo-ANM.…”

Section: Introductionmentioning

confidence: 99%

SAR Tomography Based on Atomic Norm Minimization in Urban Areas

Liu

Peng

et al. 2022

Remote Sensing

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Synthetic aperture radar (SAR) tomography (TomoSAR) is a powerful tool for the three-dimensional (3D) reconstruction of buildings in urban areas. At present, the compressed sensing (CS) technique has been widely used in the TomoSAR inversion of urban areas because of the sparsity of the backscattering power of buildings along the elevation direction. However, this algorithm discretizes the elevation and assumes that the scatterers are located on predetermined finite grids. In fact, scatterers can lie anywhere in the elevation direction, regardless of grid point constraints. The phenomenon of scatterer positioning errors due to elevation discretization is called the off-grid effect, which will affect the height estimation accuracy of TomoSAR. To overcome this problem, we proposed a TomoSAR reconstruction algorithm based on atomic norm minimization (Tomo-ANM) in this paper. Tomo-ANM employs ANM, a continuous compressed sensing technique, to obtain scatterer positions on the continuous dictionary, thus eliminating the off-grid effect. Baseline compensation is necessary to obtain the data of virtual uniform baselines or the samples of uniform data during preprocessing. A fast realization of ANM, IVDST, is utilized to accelerate the process. Tomo-ANM was tested through simulation experiments, and the results confirmed the validity of eliminating the influence of off-grid effects and exhibited an improved location accuracy and detection rate in less time compared with the on-grid TomoSAR algorithm SL1MMER. Real data experiments based on eight staring spotlight TerraSAR-X images showed that Tomo-ANM can improve the accuracy of building height estimation by 4.83% relative to its real height.

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Section: Introductionmentioning

confidence: 99%

SAR Tomography Based on Atomic Norm Minimization in Urban Areas

Liu

Peng

et al. 2022

Remote Sensing

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“…Recently, with the development of sparse recovery (SR) techniques, sparse recovery based space-time adaptive processing (SR-STAP) methods have been extensively re-searched [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. By utilizing the intrinsic sparsity of the clutter in angle-Doppler plane, SR-STAP recovers a signal with a sparse coefficient vector and a uniformly discretized space-time dictionary.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage STAP Method Based on Fine Doppler Localization and Sparse Bayesian Learning in the Presence of Arbitrary Array Errors

Liu

Wang

et al. 2021

Sensors

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In the presence of unknown array errors, sparse recovery based space-time adaptive processing (SR-STAP) methods usually directly use the ideal spatial steering vectors without array errors to construct the space-time dictionary; thus, the steering vector mismatch between the dictionary and clutter data will cause a severe performance degradation of SR-STAP methods. To solve this problem, in this paper, we propose a two-stage SR-STAP method for suppressing nonhomogeneous clutter in the presence of arbitrary array errors. In the first stage, utilizing the spatial-temporal coupling property of the ground clutter, a set of spatial steering vectors with array errors are well estimated by fine Doppler localization. In the second stage, firstly, in order to solve the model mismatch problem caused by array errors, we directly use these spatial steering vectors obtained in the first stage to construct the space-time dictionary, and then, the constructed dictionary and multiple measurement vectors sparse Bayesian learning (MSBL) algorithm are combined for space-time adaptive processing (STAP). The proposed SR-STAP method can exhibit superior clutter suppression performance and target detection performance in the presence of arbitrary array errors. Simulation results validate the effectiveness of the proposed method.

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“…Here, some typical SR‐STAP algorithms are developed, such as iterative adaptive algorithm (IAA) [12], fast converging sparse Bayesian learning (FCSBL) [13], robust knowledge‐aided sparse recovery (RKASR) [14], and parameter‐searched orthogonal matching pursuit (PSOMP) [15]. Conventional SR‐STAP algorithms can drastically decrease the training sample requirements, but they usually cannot thoroughly solve the off‐grid problem even if the grid mismatch correction is done [15] or a gridless technique [16] is used. As all these SR‐STAP algorithms are fully space‐time algorithms, they often have huge computational workload when the space‐time DoF of airborne STAP radar is high.…”

Section: Introductionmentioning

confidence: 99%

A generalised eigenvalue reweighting covariance matrix estimation algorithm for airborne STAP radar in complex environment

Xiao

Wang

Wen

et al. 2021

IET Radar Sonar & Navi

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To improve the space-time adaptive processing (STAP) performance of airborne radar in complex environments, a generalised eigenvalue reweighting covariance matrix estimation algorithm called GERCM is proposed here. First, the interference plus noise (IPN) covariance matrix of cell under test (CUT) data is estimated by the selected target-free training samples around the CUT with the sample covariance matrix method. Then, with the component decompositions of the selected training samples and the assumption of approximately equal subspace, the IPN covariance matrix of CUT data is reformulated by the eigenvector matrix, eigenvalue matrix, and the eigenvalue reweighting vector. Subsequently, based on the modified covariance matching estimation criterion, the eigenvalue reweighting vector is estimated by solving the redesigned convex optimisation problem with the Lagrange dual method. Finally, the STAP weight vector is calculated to process the CUT data. The proposed algorithm can obtain a relatively accurate IPN covariance matrix of CUT data by sufficiently utilising the non-homogeneous training samples and can effectively protect the moving targets in CUT data, which can be applied to airborne radar with arbitrary array structure and antenna configuration. Simulation results and performance analyses based on the multi-channel airborne radar measurement data demonstrate the effectiveness of the proposed GERCM algorithm.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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A Grid-Less Total Variation Minimization-Based Space-Time Adaptive Processing for Airborne Radar

Cited by 11 publications

References 31 publications

SAR Tomography Based on Atomic Norm Minimization in Urban Areas

SAR Tomography Based on Atomic Norm Minimization in Urban Areas

A Two-Stage STAP Method Based on Fine Doppler Localization and Sparse Bayesian Learning in the Presence of Arbitrary Array Errors

A generalised eigenvalue reweighting covariance matrix estimation algorithm for airborne STAP radar in complex environment

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