Open AccessHRRP Target Recognition Based on Sparse Representation
Author(s) -
Duan Peipei,
Yan Zhang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1237/3/032021
Subject(s) - sparse approximation , pattern recognition (psychology) , artificial intelligence , computer science , radar , representation (politics) , range (aeronautics) , noise reduction , engineering , telecommunications , politics , political science , law , aerospace engineering
When high resolution range profiles(HRRP) are used to recognize radar target, a few traditional recognition methods analyze the sparseness of HRRP samples. In order to overcome the large sample size problem and simplify the recognition procedure, sparse representation is an effective way to compress HRRP samples and extract the features. Thus, a structure redundant dictionary and a fast sparse representation algorithm are introduced to implement radar target recognition here. The simulation results show that this algorithm has higher recognition rate and better denoising performance. It is easy and practical for radar target recognition.