AI 中文总结
针对混合噪声下确定性最大似然测向中ECM算法收敛不当的问题，设计ECME算法并推导DOA估计的CRLB，仿真表明该算法收敛性好且估计误差渐近接近CRLB。
AI 中文摘要
球不变（SI）随机过程可用于建模脉冲噪声和不可靠测量。近期，高斯与SI分量的混合噪声已被应用于确定性最大似然测向中，在此背景下，期望条件最大化（ECM）算法——期望最大化算法的扩展——已被应用并设计。然而，仿真结果表明，ECM算法总是出现不当收敛的问题。本文应用并设计了ECM Either（ECME）算法，它是ECM算法的扩展，该算法在每次迭代时额外利用实际对数似然函数来首先更新部分参数估计，且无需初始化所有参数估计。此外，推导了波达方向（DOA）估计器的确定性克拉美罗下界（CRLB）并进行了比较。仿真结果表明，ECME算法表现出恰当的收敛性，且其DOA估计的均方根误差随信号功率增加渐近趋近于CRLB，即推导的CRLB是正确的。
英文摘要
Spherically invariant (SI) random processes can model impulsive noise and unreliable measurements. Recently, the mixture noise of Gaussian and SI components has been used in deterministic maximum likelihood direction finding. In this context, the Expectation-Conditional Maximization (ECM) algorithm, an extension of the expectation-maximization algorithm, has been applied and designed. However, simulation results show that the ECM algorithm always improperly converges. In this article, the ECM Either (ECME) algorithm, an extension of the ECM algorithm, is applied and designed, which additionally utilizes the actual log-likelihood function to first update partial parameter estimates at every iteration and does not need to initialize all parameter estimates. Moreover, the deterministic Cramer-Rao low bounds (CRLBs) of DOA estimators are derived and compared. Simulation results indicate that the ECME algorithm exhibits proper convergence and its root mean square errors of DOA estimates asymptotically approach the CRLBs as the signal powers increase, i.e., the derived CRLBs are correct.
