Ocean duct inversion from radar clutter using Bayesian-Markov chain Monte Carlo method

Abstract

Using the Bayesian-Markov chain Monte Carlo （MCMC） method, based on the measurement information of radar clutter（electromagnetic propagation loss）,we obtain the posterior probability density of the duct parameter by describing the prior information of the duct parameter as the prior probability density. And then, Gibbs sampler of the MCMC method is used to sample the posterior probability density. The sample maximal likelihood is regarded as an evaluation of the duct parameter distribution. The results of simulation experiment show that this set of methods make good use of the prior information and the inversion precise is better than the genetic algorithm. In addition, it is capable of describing （definite or indefinite） prior information in a convenient and controllable way, as well as capable of giving the complete solutions, which is very important to practical applications.