A New MCMC Sampling Based Segment Model for Radar Target Recognition

One of the main tools in radar target recognition is high resolution range profile (HRRP)‎. ‎However‎, ‎it is very sensitive to the aspect angle‎. ‎One solution to this problem is to assume the consecutive samples of HRRP identically independently distributed (IID) in small frames of aspect angles‎, ‎an assumption which is not true in reality‎. ‎However, b‎‎ased on this assumption‎, ‎some models have been developed to characterize the sequential information contained in the multi-aspect radar echoes‎. ‎Therefore‎, ‎they only consider the short dependency between consecutive samples‎. ‎Here‎, ‎we propose an alternative model‎, ‎the segment model‎, ‎to address the shortcomings of these assumptions‎. ‎In addition‎, ‎using a Markov chain Monte-Carlo (MCMC) based Gibbs sampler as an iterative approach to estimate the parameters of the segment model‎, ‎we will show that the proposed method is able to estimate the parameters with quite satisfying accuracy and computational load‎.