非均匀噪声场下的随机最大似然测向
Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields
AI总结：
针对未知非均匀噪声下相关随机源的最大似然测向问题，提出基于EM算法推广的ECME算法，通过显式公式更新协方差估计并保证正定性，兼具计算效率与稳定性，仿真验证了其有效性。
摘要：
在这篇快报中，我们采用并设计了期望-条件最大化任选（ECME）算法——EM算法的一种推广形式，用于求解未知非均匀噪声下可能相关的随机源的最大似然测向问题。与交替最大化不同，ECME算法通过显式公式更新源协方差矩阵和噪声协方差矩阵的估计值，且能保证二者分别为半正定和正定。因此，ECME算法计算高效且运行稳定。仿真结果验证了该算法的有效性。
英文摘要：
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.
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