Space-Based Radar Clutter Suppression Algorithm Based on Phase Error Calibration - Wu, Jiaye; Zhang, Shuangxi; Liu, Weijian; Mei, Shaohui; Zhang, Zhaojian; Wang, Yong-Liang; Huang, Jun (IEEE transactions on aerospace and electronic systems, 2025)
Space-time adaptive processing (STAP) has proven to be a critical technique for clutter suppression in space-based radar (SBR) systems. For SBR, platform high-speed motion and the large antenna apertures induce baseline geometric distortions, creating range-varying space-time coupled phase errors. These errors interact with inherent channel amplitude-phase errors, significantly aggravating channel decorrelation effects and degrading clutter covariance matrix (CCM) estimation accuracy, ultimately leading to severe performance degradation of STAP processors. Existing studies primarily analyze error impacts on STAP performance, effective calibration methods to enhance clutter suppression performance remain underdeveloped. This paper proposes a clutter suppression algorithm for SBR based on phase error calibration. A three-dimensional space-time-baseline coupling model under Earth rotation constraints is established, which reveals the range-dependent propagation characteristics of phase errors and their destructive mechanisms on CCM estimation. Based on this foundation, a two-stage processing framework is constructed. In the first stage, the joint processing of orthogonal coded waveform design and compressed sensing (CS) is implemented to achieve near-range nonstationary clutter suppression and ambiguous range cell decoupling. The second stage develops a hierarchical error calibration mechanism. Initially, a coarse correlation-based estimator is built by exploiting the local stationarity of phase errors within ambiguous range cells to extract locally consistent features. Subsequently, a full-range phase error characterization framework with low-order polynomial parameterization is established, where high-precision error estimation and compensation across all range dimensions are realized through the model coefficient optimization. Simulation results demonstrate that the proposed algorithm effectively calibrates the impact of phase errors on CCM estimation accuracy, resulting in significant improvement of clutter suppression performance in subsequent STAP processing.