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中文摘要：
针对Bayesian方位估计计算量大的问题，将马尔可夫蒙特卡罗方法与Bayesian方位估计相结合，提出一种基于MH（Metropolis—Hastings）抽样的Bayesian方位估计新方法（简称MHB）。该方法将Bayesian算法的空间谱函数作为信号的概率分布函数，并利用MH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Bayesian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 was known to have the best performance in DOA estimation of narrowband sources. However, it entailed heavy computation. In order to reduce computational complexity, the combination of Markov Monte Carlo methods with Bayesian estimator was explored. A novel Bayesian DOA Estimator based on Metropolis- Hasting Sampling （MHB） was proposed. MHB regarded the power of the MHB spectrum function as the target distribution up to a constant proportionality, and used 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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