Space time adaptive processing based on sparse recovery and clutter reconstructing

• Published in: IET radar, sonar & navigation sonar & navigation, 2019-05, Vol.13 (5), p.789-794
• Main Authors: Zhang, Wei, He, Zishu, Li, Huiyong
• Format: Article
• Language: English
• Subjects: airborne radar; angular‐Doppler plane; array radar system; available training samples; clutter covariance matrix; clutter patches; clutter return; clutter suppression; conventional SR‐based STAP algorithms; covariance matrices; crucial technique; Doppler radar; extreme heterogeneous case; missed clutter components; object detection; phased array radar; radar clutter; radar detection; radar signal processing; Research Article; secondary data sample; space time adaptive processing; sparse recovery technique; spatial frequency; strong clutter background; target detection performance; training sample
• ISSN: 1751-8784; 1751-8792; 1751-8792
• DOI: 10.1049/iet-rsn.2018.5307

For an airborne-phased array radar system, space-time adaptive processing (STAP) is supposed to be a crucial technique for improving its target detection performance in a strong clutter background. Here, the authors consider the extreme heterogeneous case, i.e. the number of available training samples is limited to one. With only one training sample, the sparse recovery (SR) technique is utilized to estimate the clutter patches in the angular-Doppler plane, i.e. the pair of Doppler frequency and spatial frequency corresponding to the grid in angular-Doppler plane is determined. Based on the observation that some clutter components may be missed for one secondary data sample, the clutter covariance matrix (CCM) is estimated through reconstructing some missed clutter components by utilising the symmetric property of clutter return. From the simulation results, the proposed approach can achieve great performance of clutter suppression with only one training sample compared with conventional SR-based STAP algorithms.