KFD-Based Multiclass Synthetical Discriminant Analysis for Radar HRRP Recognition

Abstract

A new extended application of kernel Fisher discriminant (KFD), i.e., KFD-based multiclass synthetical discriminant analysis (KFD-MSDA), is proposed for multi-target discrimination by using the traditional KFD “one-against-one”, and for multi-target classification under the proposed minimum synthetical Euclidian distance (MSED) rule. Theoretical analysis and experimental results on the measured and simulated radar HRRP databases indicate that, compared with several classical kernel-based methods, KFD-MSDA not only performs better with more stable recognition, but also keeps lower training time consuming and competitive recognition speed.