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中文摘要：
针对贝叶斯方位估计方法计算量大的问题,将马尔可夫蒙特卡罗方法与贝叶斯方位估计方法相结合,提出一种基于Metropolis-Hastings抽样的贝叶斯方位估计方法（Bayesian DOA Estimator Basedon Metropolis-Hasting Sampling,简称MHB）。MHB方法将贝叶斯算法的空间谱函数作为信号的概率分布函数,并利用Metropolis-Hastings抽样方法从该概率分布函数中抽样。研究结果表明,MHB方法不但保持了贝叶斯方位估计方法的优良性能,而且大大减小了计算量。
英文摘要：
Bayesian estimator is known to have the best performance in DOA estimation of narrowband sources.However,it entails heavy computation.In order to reduce computational complexity,the comhination of Markov Monte Carlo methods with Bayesian estimator is explored.A novel Bayesian DOA Estimator based on Metropolis-Hasting Sampling （MHB） is proposed in this paper. MHB regards the power of the MHB spectrum function ＂as＂ the target distribution up to a constant proportionality,and uses Metropolis-Hasting sampler to sample from it.Simulations show that MHB not only keeps the excellent performance of Bayesian estimator but also reduces computation greatly.
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同项目期刊论文
An Adaptive Differentially Coherent Detection and Phase Compensation Algorithm in Underwater Acousti
Real-time Parallel Processing System Design and Implementation for Underwater Acoustic Communication
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