Radar HRRP Target Recognition Based on Truncated Stick-breaking Hidden Markov Model

To improve the performance of radar High-Resolution Range Profile (HRRP) target recognition, a new Truncated Stick-Breaking Hidden Markov Model (TSB-HMM) based on time domain feature is proposed. Moreover, a hierarchical classification scheme based on TSB-HMM is employed, which utilizes both time domain feature and power spectral density feature of HRRPs for hierarchical recognition. Experimental results based on measured data show that the TSB-HMM is an effective method for radar HRRP recognition, and the hierarchical classification scheme can largely enhance the average recognition rate. Furthermore, the proposed method can obtain satisfactory recognition performances even with very limited training data.