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中文摘要：
针对短采样宽带信号近似最大似然方位估计（AML）计算量大的问题，将马尔可夫蒙特卡罗（MC—MC）方法与近似最大似然方位估计相结合，提出一种基于完美抽样的近似最大似然方位估计快速算法（PAML）。该算法将AML算法的空间谱函数作为信号的概率分布函数，并利用完美抽样方法从该概率分布函数中抽样。与AML和遗传算法的对比实验研究表明，两目标情况下PAML算法在中低信噪比条件下的估计性能与AML和遗传算法性能相当，而计算量分别是二者的1／24和1／3。随着目标个数的增加，PAML算法的计算量优势将更加明显。
英文摘要：
Approximated maximum likelihood estimator （AML） has been shown to be the best performance in short sampling wideband sources DOA estimation. However, the computation burden of AML is very large. In order to lighten computation burden, Markov Monte Carlo （MCMC） methods are combined with approximated maximum likelihood DOA estimator. A novel approximated maximum likelihood DOA estimator based on perfect sampling （PAML） is proposed. PAML regards the power of the AML spectrum function as the target distribution up to a constant proportionality, and uses perfect sampler to sample from it. The performance of AML and genetic algorithm （GA） are compared with PAML through computer simulations. The simulations show that on the condition of two sources, the PAML provides similar performance to those achieved by the AML and GA in low and mid SNR ranges, but its computational cost is only 1/24 of AML or 1/3 of GA. With the number of sources rising, the advantage of computational reduction of PAML is even more prominent.
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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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