EM-Type Algorithms for DOA Estimation in Unknown Nonuniform Noise

TL;DR

This paper introduces efficient EM-type algorithms, including GEM and SAGE variants, for DOA estimation in unknown nonuniform noise, demonstrating improved performance over traditional methods through simulations.

Contribution

The paper develops sequential EM-type algorithms tailored for deterministic and stochastic ML DOA estimation in challenging noise conditions, outperforming existing approaches.

Findings

SAGE algorithm outperforms GEM in deterministic ML estimation

Sequential DOA updates improve stochastic ML estimation accuracy

Simulation results validate the effectiveness of proposed algorithms

Abstract

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.