Open-set HRRP recognition method based on joint sparse representation

Abstract

Abstract Aiming at the problem of multi-view high-resolution range profile (HRRP) target recognition under open set conditions, we proposed an open set recognition method based on joint sparse representation (JSR), which solves the problem of low recognition rate of traditional methods under open set conditions. This method is applied to the background of radar single-station observation. JSR is used to solve the reconstruction error of multi-view HRRP by the over-complete dictionary, while extreme value theory (EVT) is used to model the reconstruction error tailing of matching and non-matching categories and transform the open set identification problem into a hypothesis testing problem. During recognition, we use the reconstruction error to determine the candidate class, the scores of the matching class and non-matching class are obtained according to the confidence of tail distribution, and the weighted sum of the two is used as the category criterion to finally determine the target or candidate class outside the library. This method can effectively use the relevant information between multi-view HRRPs to improve the performance of HRRP recognition under open set conditions. The algorithm is tested with HRRP data generated from MSTAR inversion, and the results show that the performance of the proposed method is better than the mainstream open set recognition method.