research-article

Robust SR-STAP algorithms in partly calibrated arrays for airborne radar

Authors:

Weijun HuangAuthors Info & Claims

Volume 219, Issue C

https://doi.org/10.1016/j.sigpro.2024.109389

Published: 25 June 2024 Publication History

Highlights

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A novel space-time signal model for STAP radar based o n subarray based partly calibrated arrays (PCA) is developed.

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The grid-based sparse representation of the signal model for the PCAs is presented.

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We proposed a novel SR-STAP algorithm utilizing the low rank and block-sparsity promoting ℓ *, 1, mixed-norm minimization along with an equivalent compact expression, termed as joint Low Rank and Block Sparse Recovery based STAP (LRBSR-STAP).

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We proposed a gridless implementation of our LRBSR formulation for the special case of identical linear subarrays with a common baseline, referred to as GridLess joint Low Rank and Block Sparse Recovery based STAP (GL-LRBSR-STAP).

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Extensive numerical experiments are conducted, and the results shows that the proposed algorithms consistently and substantially outperform all the other comparative algorithms with inter-subarray gain-phase and displacement errors under small sample conditions.

Abstract

Sparse recovery based space-time processing (SR-STAP) techniques are capable of achieving satisfactory clutter suppression and target detection performance, even with limited training samples. The majority of existing approaches are developed under the assumption of ideal uniform linear arrays, where no imperfections are present. In this study, we consider the robust STAP problem in the context of airborne partly calibrated array (PCA) composed of several well calibrated subarrays with unknown inter-subarray gain-phase and displacement errors. Initially, the space-time signal for airborne STAP radar based on PCAs is modeled. Subsequently, the authors propose two robust SR-STAP algorithms, considering both grid-based and gridless methods, leveraging the block-sparsity and low rank structural characteristics inherent in the signal model. Extensive numerical experiments have shown the significant performance benefits including small sample applicability and resilience to inter-subarray gain-phase and displacement errors.

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Published In

cover image Signal Processing

Signal Processing Volume 219, Issue C

Jun 2024

404 pages

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Elsevier B.V.

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 25 June 2024

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