Hybrid hidden markov modeling for target recognition based on high-resolution radar signal

Hidden Markov Model (HMM) is a commonly used modeling approach for the radar target recognition based on high-resolution range profile (HRRP). The HMM describes HRRP with the assumption of Gaussian observation probability, which results in the inaccurate description due to the non-Gaussian property of HRRP. To this end, we propose a hybrid HMM framework, which uses an extra model to fit the observation probability rather than making distribution assumptions. The extra model estimates the posterior probability of the state, and then can calculate the pseudo observation probability, which in turn modulates the other model parameters in HMM. The interplay between HMM and the extra model ultimately leads to a better description for observed data. In this paper, we use the support vector machine (SVM) and deep neural network (DNN) separately as the extra model and validate the superiority of our hybrid HMM in HRRP-based target recognition.