相干信源波达方向估计的广义最大似然算法

论文基于广义导向矢量和广义阵列流形矩阵，建立了多相干源(组)情况下的阵列数据模型，然后提出了波达方向估计的广义最大似然算法。对于广义最大似然算法，入射信源可以是多相干源(组)，阵列的几何结构也没有任何约束，而且它分辨的信源数还可以大于阵元数。随后，论文将广义最大似然算法与常规最大似然算法进行了理论比较，并给出了广义最大似然竹法方位估计一致性的证明和方位估计方差的计算公式。理论分析表明，在空间只存在非相干信源时，广义最大似然算法与常规的最大似然算法是等价的，而在空间存在多相干源(组)时，它的性能较常规最大似然算法有较大的改进，方位估计的方差更小。最后论文利用遗传算法实现了广义最大似然算法，并通过MonteCarlo仿真实验证明了广义最大似然算法的有效性。

Generalized Maximum Likelihood Algorithm for Direction-of-Arrival Estimation of Coherent Sources

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 array's 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 DML's 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.