利用Bayesian-MCMC方法从雷达回波反演海洋波导

应用贝叶斯-蒙特卡罗（Bayesian-MCMC）方法将海洋波导参数的先验信息描述为先验概率密度，结合雷达回波资料（电磁波传播损耗），得到待反演海洋波导参数的后验概率密度，用马尔可夫链蒙特卡罗（MCMC）-Gibbs采样器采样后验概率密度分布，并用样本最大似然估计值作为对海洋波导参数分布的估计.数值实验结果表明，该方法对先验信息进行了有效利用，反演精度高于遗传算法的反演精度.该方法较为充分利用先验信息，得到解的概率分布，即解的不确定性分析，这在实际应用中有一定的参考价值.关键词：波导电磁波传播损耗贝叶斯-蒙特卡罗概率分布

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

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.