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MIMO radar target localization via Markov Chain Monte Carlo optimization

In this paper, we focus on the problem of target localization in distributed multiple-input multiple-output (MIMO) radar, where the range measurements are the sum of transmitter-to-target and target-to-receiver distances. To determine the target position, this paper presents a Bayesian approach, in which a Bayesian model is derived for the noisy range measurements and thus the posterior distribution of the unknown target position parameters is defined. However, this complicated distribution is unhelpful for sampling directly. To solve it, this paper applies the Markov Chain Monte Carlo (MCMC) method to estimate the corresponding posterior distribution and draws samples via Gibbs sampling. The performance of the developed algorithm is demonstrated via computer simulation.