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：在这封信中,我们使用并设计了预期条件最大化的算法(ECME),即EM算法的概括化,以解决在未知的不统一噪音下可能相互关联的随机源的最大可能的方向问题。与交替最大化不同的是,ECME算法通过明确的公式更新了源和噪音共变矩阵估计数,并能够保证这两种估计数都是正半定值和确定值。因此,ECME算法在计算上是有效的,在操作上是稳定的。模拟结果证实了算法的有效性。