摘要
To relax the target aspect sensitivity and use more statistical information of the High Range Resolution Profiles (HRRPs), in this paper, the average range profile and the variance range profile are extracted together as the feature vectors for both training data and test data representa-tion. And a decision rule is established for Automatic Target Recognition (ATR) based on the mini-mum Kullback-Leibler Distance (KLD) criterion. The recognition performance of the proposed method is comparable with that of Adaptive Gaussian Classifier (AGC) with multiple test HRRPs, but the proposed method is much more computational efficient. Experimental results based on the measured data show that the minimum KLD classifier is effective.
To relax the target aspect sensitivity and use more statistical information of the High Range Resolution Profiles （HRRPs）, in this paper, the average range profile and the variance range profile are extracted together as the feature vectors for both training data and test data representation. And a decision rule is established for Automatic Target Recognition （ATR） based on the minimum Kullback-Leibler Distance （KLD） criterion. The recognition performance of the proposed method is comparable with that of Adaptive Gaussian Classifier （AGC） with multiple test HRRPs, but the proposed method is much more computational efficient. Experimental results based on the measured data show that the minimum KLD classifier is effective.
基金
Partially supported by the National Natural Science Foundation of China (No.60302009).
