Subspace Methods of Radar Target Recognition Using Range Profiles
子空间法雷达目标一维像识别研究

Eigen-subspace and canonical-subspace methods are studied and applied to feature-extraction for target recognition using range profiles of a High-range-Resolution-Radar (HRR) system. Based on this study, a subspace cluster method is proposed to tackle the problem of aspect-sensitivity of range profiles. In subspace cluster method, the aspect scope of a radar target is divided into a proper number of zones, and eigen-subspaces are established for each zone. After the zone number of an unknown target is determined by radar, the range profile of this target is mapped into eigen-subspaces of the corresponding zone, and the class whose subspace has the maximum mapping energy is judged as the right class to which the unknown target belongs. This method is named as single-mode classification rule in the subspace cluster method. Experimental results on simulated data and field data show the efficiency of the subspace methods and subspace cluster method in target recognition.