A Refuse-recognition Method for Rardar HRRP Target Recognition Based on Mahalanobis Distance
In the radar automatic target recognition (RATR) system using high resolution range profile (HRRP), if a test sample has not been trained in training phase, it would lead to a full miss classification in test phase. In this paper, we design a classifier based on generalized confidence, which can efficiently refuse-recognize a new target. Firstly, principal component analysis (PCA) method is used to extract feature vector from every sample. Secondly, the classifier calculates each feature vectors generalized confidence on mahalanobis distance. Consequently, the distribution of confidence is used to define a refuse-recognition threshold for each training target. In test phase, for each trained-target in the database, we suppose that the test sample belongs to current target, calculate the generalized confidence, judge whether the test sample really belongs to the target or not via comparing the confidence with the targets refuse-recognition threshold. The final class is determined by vote. The experimental results demonstrate the effectiveness of the proposed algorithms.
generalized confidence measure refuse-recognize HRRP mahalanobis distance
Liao Kuo Jiansheng Fu Yang wanlin
School of Electronic Engineering University of Electronic Science and Technology of China Chengdu, China
国际会议
太原
英文
503-506
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）