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- Title
A New MCMC Sampling Based Segment Model for Radar Target Recognition.
- Authors
HADAVI, Mahdi; RADMARD, Mojtaba; NAYEBI, Mohammad Mahdi
- 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 independently distributed (HD) in small frames o f 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.
- Subjects
MARKOV chain Monte Carlo; STATISTICAL sampling; RADAR target recognition; GIBBS' free energy; PARAMETER estimation
- Publication
Radioengineering, 2015, Vol 24, Issue 1, p280
- ISSN
1210-2512
- Publication type
Article
- DOI
10.13164/re.2015.0280