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A Structured Constraint-Based RPCA Algorithm for Ground Moving Target Detection and Parameter Estimation
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DOI：10.1109/lgrs.2026.3713514.png)
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Abstract
En 中文
Robust principal component analysis (RPCA) exploits target characteristics in multichannel synthetic aperture radar ground moving target indication (SAR-GMTI) to decompose the observed data matrix into a low-rank clutter matrix and a sparse target matrix. However, during the optimization process, conventional RPCA algorithms may distort the target phase information, resulting in the separation of target detection and parameter estimation. In addition, strong scatterers may lead to a high false alarm rate in target detection. To address these challenges, a structured constraint-based RPCA (SCRPCA) algorithm is proposed. On the one hand, the rank-one property of clutter is utilized to replace the conventional low-rank constraint; on the other hand, the sparse target component is reformulated as the product of amplitude and phase terms, enabling simultaneous target detection and parameter estimation. Furthermore, the along-track interferometry (ATI) phase is incorporated as a weighting factor to suppress clutter energy leakage. The block coordinate descent (BCD) algorithm is employed to solve the proposed optimization model. Experimental results using real measured data demonstrate the effectiveness of the proposed method.
Keywords:
Along-track interferometry (ATI)
ground moving target indication (GMTI)
robust principal component analysis (RPCA)
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