一种基于混合MCMC的时域波达方向估计算法

基于马尔科夫链蒙特卡洛（Markov Chain Monte Carlo,MCMC）方法的时域波达方向估计算法通过构造马尔科夫链的方式来对波达方向进行估计，但是现有的算法在马尔科夫链的收敛速度和结果上并没有表现出很好的鲁棒性。为了优化算法的性能，采用多(短)链并行的方式代替原来的长链生成方式，提高了算法收敛的稳定性；并对特定模型下的构造过程进行分析，优化了状态空间，提高了算法的搜索效率；同时结合多混合的MCMC方法，进一步提高了算法估计的精确度和收敛速度。仿真结果表明，改进后的算法对波达方向估计的准确性和实时性都有很大提升。

A time-domain direction of arrival estimation algorithm based on hybrid Markov chain Monte Carlo(MCMC)

The time-domain direction of arrival(DOA) estimation algorithm based on Markov Chain Monte Carlo(MCMC) method estimates the DOA by constructing a Markov chain,but the existing algorithms do not show good robustness in the convergence speed and results of the Markov chain.In order to optimize the performance of the algorithm,a multiple(short) chain parallel method is used to replace the original long chain generation method,thus improving the stability of the algorithm convergence.The construction process under a specific model is analyzed and the state space is optimized,thus improving the search efficiency of the algorithm.The multi-hybrid MCMC method further improves the accuracy and convergence speed of algorithm estimation.The simulation results show that the improved algorithm has greatly improved the accuracy and real-time performance of the DOA estimation.