论文标题
不明噪声中DOA估计的EM类型算法
EM-Type Algorithms for DOA Estimation in Unknown Nonuniform Noise
论文作者
论文摘要
期望 - 最大化（EM）算法同时更新所有参数估计，这不适用于未知的非均匀噪声中的到达方向（DOA）估计。在这项工作中，我们介绍了几种有效的EM-Type算法，这些算法依次更新参数估计，以解决确定性和随机最大值 - 样式（ML）方向，发现在不明的不均匀噪声中发现问题。具体而言，我们设计了一种通用的EM（GEM）算法和用于计算确定性ML估计量的空间范围的广义EM（SAGE）算法。仿真结果表明，SAGE算法的表现优于GEM算法。此外，我们设计了两种用于计算随机ML估计量的SAGE算法，其中第一个更新DOA估计值同时更新，而第二个则更新DOA估计值。仿真结果表明，第二SAGE算法的表现优于第一个算法。
The expectation--maximization (EM) algorithm updates all of the parameter estimates simultaneously, which is not applicable to direction of arrival (DOA) estimation in unknown nonuniform noise. In this work, we present several efficient EM-type algorithms, which update the parameter estimates sequentially, for solving both the deterministic and stochastic maximum--likelihood (ML) direction finding problems in unknown nonuniform noise. Specifically, we design a generalized EM (GEM) algorithm and a space-alternating generalized EM (SAGE) algorithm for computing the deterministic ML estimator. Simulation results show that the SAGE algorithm outperforms the GEM algorithm. Moreover, we design two SAGE algorithms for computing the stochastic ML estimator, in which the first updates the DOA estimates simultaneously while the second updates the DOA estimates sequentially. Simulation results show that the second SAGE algorithm outperforms the first one.
