一种基于MH抽样的极化域—空域联合谱MUSIC估计算法

针对联合谱MUSIC算法中谱峰搜索计算量大的问题,通过运用马尔可夫蒙特卡罗（MCMC）方法,提出一种计算量减小的联合谱MUSIC测向算法。该算法通过将极化联合谱MUSIC函数视为信号来向的概率密度函数,采用MCMC的MH抽样方法,能够在保持常规联合谱MUSIC算法高分辨能力的同时大大减少运算量,仿真结果验证了算法的有效性。

Polarization Space Joint Spectrum MUSIC Algorithm Via Metropolis Hastings Sampler

In order to reduce the computational complexity in polarization MUSIC spectrum peak searching,a fast direction-of-arrival（DOA） estimation method based on Markov Chain Monte Carlo and sequential searching is proposed,which regards the power of polarization MUSIC spectrum function as a target distribution up to a constant of proportionality,and uses Metropolis-Hastings（MH） sampler by eliminating spatial correlativity.The algorithm maintains the conventional MUSIC high resolution capability while reduces the computational complexity greatly,and simulation results prove the validity.