A novel sparse recovery‐based space‐time adaptive processing algorithm based on gridless sparse Bayesian learning for non‐sidelooking airborne radar

Cui, Weichen; Wang, Tong; De-gen, Wang; Zhang, Xinying

doi:10.1049/rsn2.12427

Cited by 3 publications

(1 citation statement)

References 46 publications

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“…In addition, a STAP algorithm for meshless sparse Bayesian learning, which uses the block-Toeplitz matrix to check and parameterize the SBL cost function, was developed. The proposed non-convex objective function was transformed by an iterative method, which alleviated the influence of the mesh mismatch problem [24]. A dictionary construction method, which represents an innovative method in space-time dictionary construction and differs from the traditional grid division method, was developed in [25]; however, this method has poor clutter suppression performance.…”

Section: Introductionmentioning

confidence: 99%

A Reduced Sparse Dictionary Reconstruction Algorithm Based on Grid Selection

Gao,

Zhao,

Huang

et al. 2024

Electronics

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A sparse dictionary reconstruction algorithm based on grid selection is introduced to solve the grid mismatch when using the sparse recovery space time adaptive processing (SR-STAP) algorithm. First, the atom most closely related to clutter is selected from the traditional dictionary through the spectral value dimensionality reduction method. The local mesh is divided around the selected atoms to create mesh cells, and the mesh cells that are most likely to appear in the real clutter points are judged according to the local selection iteration criteria. In this way, the mesh spacing is refined, the local mesh selection is carried out step by step, and the optimal atoms in the local region are constantly adjusted and selected to narrow the search region until the iteration termination condition is met. Finally, the space-time plane is divided using a novel meshing technique that centers around the optimal atom. By removing atoms beyond the maximum range of spatial and Doppler frequencies, the simplified sparse dictionary can overcome the mesh mismatch problem. The simulation results demonstrate that the algorithm enhances the sparse recovery accuracy of clutter space-time spectrum, mitigates the mesh mismatch effect, and boosts STAP performance.

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Section: Introductionmentioning

confidence: 99%

A Reduced Sparse Dictionary Reconstruction Algorithm Based on Grid Selection

Gao,

Zhao,

Huang

et al. 2024

Electronics

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A Data and Model-Driven Clutter Suppression Method for Airborne Bistatic Radar Based on Deep Unfolding

Huang,

Wang,

Liu

2024

Remote Sensing

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Space–time adaptive processing (STAP) based on sparse recovery achieves excellent clutter suppression and target detection performance, even with a limited number of available training samples. However, most of these methods face performance degradation due to grid mismatch, which impedes their application in bistatic clutter suppression. Some gridless methods, such as atomic norm minimization (ANM), can effectively address grid mismatch issues, yet they are sensitive to parameter settings and array errors. In this article, the authors propose a data and model-driven algorithm that unfolds the iterative process of atomic norm minimization into a deep network. This approach establishes a concrete and systematic link between iterative algorithms, extensively utilized in signal processing, and deep neural networks. This methodology not only addresses the challenges associated with parameter settings in traditional optimization algorithms, but also mitigates the lack of interpretability issues commonly found in deep neural networks. Moreover, due to more rational parameter settings, the proposed algorithm achieves effective clutter suppression with fewer iterations, thereby reducing computational time. Finally, extensive simulation experiments demonstrate the effectiveness of the proposed algorithm in clutter suppression for airborne bistatic radar.

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An Efficient Sparse Recovery STAP Algorithm for Airborne Bistatic Radars Based on Atomic Selection under the Bayesian Framework

Liu,

Wang,

Huang

2024

Remote Sensing

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The traditional sparse recovery (SR) space-time adaptive processing (STAP) algorithms are greatly affected by grid mismatch, leading to poor performance in airborne bistatic radar clutter suppression. In order to address this issue, this paper proposes an SR STAP algorithm for airborne bistatic radars based on atomic selection under the Bayesian framework. This method adopts the idea of atomic selection for the process of Bayesian inference, continuously evaluating the contribution of atoms to the likelihood function to add or remove atoms, and then using the selected atoms to estimate the clutter support subspace and perform sparse recovery in the clutter support subspace. Due to the inherent sparsity of clutter signals, performing sparse recovery in the clutter support subspace avoids using a massive number of atoms from an overcomplete space-time dictionary, thereby greatly improving computational efficiency. In airborne bistatic radar scenarios where significant grid mismatch exists, this method can mitigate the performance degradation caused by grid mismatch by encrypting grid points. Since the sparse recovery is performed in the clutter support subspace, encrypting grid points does not lead to excessive computational burden. Additionally, this method integrates out the noise term under a new hierarchical Bayesian model, preventing the adverse effects caused by inaccurate noise power estimation during iterations in the traditional SR STAP algorithms, further enhancing its performance. Our simulation results demonstrate the high efficiency and superior clutter suppression performance and target detection performance of this method.

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A novel sparse recovery‐based space‐time adaptive processing algorithm based on gridless sparse Bayesian learning for non‐sidelooking airborne radar

Cited by 3 publications

References 46 publications

A Reduced Sparse Dictionary Reconstruction Algorithm Based on Grid Selection

A Reduced Sparse Dictionary Reconstruction Algorithm Based on Grid Selection

A Data and Model-Driven Clutter Suppression Method for Airborne Bistatic Radar Based on Deep Unfolding

An Efficient Sparse Recovery STAP Algorithm for Airborne Bistatic Radars Based on Atomic Selection under the Bayesian Framework

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