In this letter, we employ and design the expectation--conditional maximization either (ECME) algorithm, a generalisation of the EM algorithm, for solving the maximum likelihood direction finding problem of stochastic sources, which may be correlated, in unknown nonuniform noise. Unlike alternating maximization, the ECME algorithm updates both the source and noise covariance matrix estimates by explicit formulas and can guarantee that both estimates are positive semi-definite and definite, respectively. Thus, the ECME algorithm is computationally efficient and operationally stable. Simulation results confirm the effectiveness of the algorithm.

翻译：摘要：本文采用并设计了期望-条件最大化扩展（ECME）算法（EM算法的一种推广），用于解决未知非均匀噪声环境下可能相关的随机源最大似然测向问题。与交替最大化不同，ECME算法通过显式公式同时更新源协方差矩阵和噪声协方差矩阵的估计值，并能够保证两者分别具有半正定性和正定性。因此，该算法具有计算高效性和运行稳定性。仿真结果验证了该算法的有效性。