联合图形约束和稳健主成分分析的地面动目标检测算法

Ground Moving Target Detection Based on Robust Principal Component Analysis and Shape Constraint

地面动目标检测是多通道合成孔径雷达系统的重要应用。稳健主成分分析的方法,因其可以将矩阵中低秩分量、稀疏分量及噪声分量分离的特性,而在多个领域得到了广泛应用。然而,该方法受到非理想误差影响,使得动目标检测结果中存在大量的杂波扰动点,从而影响动目标的检测性能。针对这一问题,该文提出一种联合稳健主成分分析和图形约束的动目标检测算法,结合系统参数对动目标区域进行形状约束,有效保证动目标检测的同时去除杂波扰动点。仿真和实测数据验证了该算法在强杂波背景下对动目标检测的有效性和可行性。

Ground moving target detection is a major application in multichannel Synthetic Aperture Radar （SAR） system. In recent years, method based on Robust Principal Component Analysis （RPCA） has attracted much attention for its good performance in distinguishing the difference among a set of correlative database. However, this kind of method might be disturbed by strong clutter points since some non-ideal factors exist. Therefore, a combined RPCA shape constraint based algorithm for moving target detection is proposed in this paper. By estimating the shape information of the moving target with system parameters, the moving target would be effectively detected, and the disturbed points would be removed at the same time. The experimental data demonstrate its good performance to detect motive target under the strong clutter background.