M‑Estimation‑Based Minimum Error Entropy with Affine Projection Algorithm for Outlier Suppression in Spaceborne SAR System

Conventional adaptive filtering algorithms often exhibit performance degradation when processing multipath interference in raw echoes of spaceborne synthetic aperture radar （SAR） systems due to anomalous outliers， manifesting as insufficient convergence and low estimation accuracy. To address this issue， this study proposes a novel robust adaptive filtering algorithm， namely the M-estimation-based minimum error entropy with affine projection （APMMEE） algorithm. This algorithm inherits the joint multi-data-block update mechanism of the affine projection algorithm， enabling rapid adaptation to the dynamic characteristics of raw echoes and achieving fast convergence. Meanwhile， it incorporates the M-estimation-based minimum error entropy （MMEE） criterion， which weights error samples in raw echoes through M-estimation functions， effectively suppressing outlier interference during the algorithm update. Both the system identification simulations and practical multipath interference suppression experiments using raw echoes demonstrate that the proposed APMMEE algorithm exhibits superior filtering performance.