Abstract:

The dictionary learning model can really reflect radar high resolution range profile (HRRP) potential structural characteristics and the statistical modeling algorithm can effectively solve the HRRP attitude sensitivity problem. Based on those features, researches on the selection of atoms and the discriminant optimization problem for label consist K-singular value decomposition （LC-KSVD） have been carried out by using statistical modeling in dividing the HRRP’s angular domain. Firstly, the maximum probability difference algorithm based on probabilistic principal component analysis is proposed to adapt HRRP angular domain to obtain the frame boundary. Secondly, based on LC-KSVD, the discriminant criterion is constructed by using the power spectrum of the frame boundary and the introduction of atomic sparse similarity error constraint in optimal dictionary selection to clarify test samples. The experimental results of the radar data show that this algorithm can improve the target recognition rate, and has good robustness to the noise interference.