Space-Time Adaptive Processing by Employing Structure-Aware Two-Level Block Sparsity

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; pp. 6386 - 6397
• Main Authors: Jiang, Zhizhuo, Wang, Xueqian, Li, Gang, Zhang, Xiao-Ping, He, You
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Airborne radar; Block sparsity; Clustering; Clutter; Covariance matrix; Detection; Doppler sonar; Echoes; Estimation; Mountains; Object detection; Radar; Radar clutter; radar clutter suppression; Radar echoes; Space-time adaptive processing; space-time adaptive processing (STAP); Spacetime; Sparsity; Statistical methods; structure-aware two-level block sparsity-based STAP (STBS-STAP); Target detection; Training
• ISSN: 1939-1404; 2151-1535; 2151-1535
• DOI: 10.1109/JSTARS.2021.3090069

Traditional radar space-time adaptive processing (STAP) cannot efficiently suppress heterogeneous clutter because of a small number of independent and identically distributed training snapshots. In the article, we propose a new STAP approach exploiting structure-aware two-level block sparsity (STBS) of radar echoes, namely STBS-STAP. It enhances the performance on clutter suppression and target detection with limited training snapshots. The clutter angle-Doppler profile always appears in a continuous diagonal clustering structure and the radar echoes at the adjacent range cells commonly share the same sparse pattern. STBS-STAP employs STBS, i.e., both the diagonal clustering structure and the common sparsity property, to acquire a precise clutter covariance matrix estimation. Thus, the new STBS-STAP achieves better performance on clutter suppression compared with existing STAP methods with a small number of training samples. Besides, STBS-STAP achieves superior target detection performance due to the precise estimation of the statistical properties of the clutter. The superiority of STBS-STAP is verified by experiments on both simulated data and measured Mountain-Top data.