High Resolution Radar Automatic Target Recognition Based On Hmm Classifier And Relax Algorithm Feature Extraction Problem Research

This thesis mainly studies the problem of Automatic Target Recognition (ATR) based on Hidden Markov Models (HMM) and high range resolution profiles (HRRP) of radar target. Our research mainly concentrates on the method of target's feature extraction and the design of classifier based on Hidden Markov Models.Based on the general models of the radar target scatter and the predigested scatter models under the special conditions, we use Relax arithmetic to extract the scatter's position information from radar HRRP as the feature vector. In particular, a HMM is employed to characterize the sequential information contained in multi-aspect high resolution range target signatures. Thus, we establish a system to identify the radar target.The computer simulation using the ISAR data of three targets shows that the identification accuracy is very good. The total identifying rate exceeds 97.35% under the condition that the HMMs contain complete information of radar target.