Polynomial segment model for radar target recognition using Gibbs sampling approach

High resolution range profile (HRRP) is a widely noted tool in radar target recognition. However, its high sensitivity to the target's aspect angle makes it necessary to seek solutions for this problem. One alternative is to assume consecutive samples of HRRP identically and independently distributed in small frames of aspect angles, an assumption which is not true in reality. Based on this simplifying assumption, some models, such as the hidden Markov model, have been developed to characterise the sequential information contained in multi-aspect radar echoes. As a result, these models consider only the short dependency between consecutive samples. Considering such issues, in this study, the authors propose an alternative polynomial segment model. In addition, using a Markov chain Monte–Carlo based Gibbs sampler as an iterative approach to estimate the parameters of the segment model, the authors will show that the results are quite satisfactory.