Abstract
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 in- dependently distributed (IID) in small frames of aspect an- gles, 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 sam- pler 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 accu- racy and computational load.
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