相干信源波達方向估計的廣義最大似然算法
Generalized Maximum Likelihood Algorithm for Direction-of-Arrival Estimation of Coherent Sources
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摘要: 論文基于廣義導向矢量和廣義陣列流形矩陣，建立了多相干源(組)情況下的陣列數(shù)據(jù)模型，然后提出了波達方向估計的廣義最大似然算法。對于廣義最大似然算法，入射信源可以是多相干源(組)，陣列的幾何結(jié)構(gòu)也沒有任何約束，而且它分辨的信源數(shù)還可以大于陣元數(shù)。隨后，論文將廣義最大似然算法與常規(guī)最大似然算法進行了理論比較，并給出了廣義最大似然竹法方位估計一致性的證明和方位估計方差的計算公式。理論分析表明，在空間只存在非相干信源時，廣義最大似然算法與常規(guī)的最大似然算法是等價的，而在空間存在多相干源(組)時，它的性能較常規(guī)最大似然算法有較大的改進，方位估計的方差更小。最后論文利用遺傳算法實現(xiàn)了廣義最大似然算法，并通過MonteCarlo仿真實驗證明了廣義最大似然算法的有效性。
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關鍵詞:
- 波達方向估計; 最大似然估計; 遺傳算法
Abstract: An original Generalized Maximum Likelihood(GML) algorithm for dircction-of-arrival estimation is proposed in this paper. A new data model is established based on generalized steering vectors and generalized array manifold matrix. For the novel GML algorithm, the incident sources may be a mixture of multi-clusters of coherent sources, the arrays geometry is unrestricted and more importantly, the number of sources resolved can be larger than the number of sensors. The comparison between the GML algorithm and conventional DML algorithm is presented based on their respective geometrical interpretation. Subsequently the estimation consistency of GML is proved and the estimation variance of GML is derived. Theoretical analysis shows that the performance of GML algorithm is consistant with DMLs in incoherent sources case, and it improves greatly in coherent source case. Using the genetic algorithm, the GML algorithm is realized in the paper, and its efficacy is proved by means of the Monte-Carlo Simulations. -
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