RADAR TARGET RECOGNITION BASED ON GENERALIZED DISCRIMINANT ANALYSIS OF QR DECOMPOSITION
基于QR分解的广义辨别分析用于雷达目标识别

A new generalized discriminant analysis(GDA) method based on QR decomposition was proposed,which would be used in radar target recognition with one-dimensional range profile.Different from the traditional approach of solving GDA by singular value decomposition(SVD),the new algorithm utilizes kernel modified Gram-Schmidt(KMGS) orthogonalization algorithm to extract the optimal transformation matrix directly,which can not only effectively hold the most discriminant information in the null space of within-class scatter matrix,but also make the solution more stable in numeric.Experiments on three measured airplains data show that the proposed method achieves better recognition performance than traditional GDA,while it has lower costs in computation partly,thereby,the real-time performance is improved.