EM and SAGE algorithms for DOA Estimation in the Presence of Unknown Uniform Noise

TL;DR

This paper extends EM and SAGE algorithms for DOA estimation to scenarios with unknown uniform noise, introducing a modified EM algorithm and analyzing their performance under different signal models.

Contribution

It proposes new EM-based algorithms for DOA estimation in unknown noise conditions, including a modified EM, and compares their stability and convergence.

Findings

SAGE outperforms EM and MEM algorithms in convergence speed.

Modified EM improves stability with unequal source powers.

SAGE requires fewer iterations for deterministic signals.

Abstract

The expectation-maximization (EM) and space-alternating generalized EM (SAGE) algorithms have been applied to direction of arrival (DOA) estimation in known noise. In this work, the two algorithms are proposed for DOA estimation in unknown uniform noise. Both the deterministic and stochastic signal models are considered. Moreover, a modified EM (MEM) algorithm applicable to the noise assumption is also proposed. These proposed algorithms are improved to ensure the stability when the powers of sources are unequal. After being improved, numerical results illustrate that the EM algorithm has similar convergence with the MEM algorithm and the SAGE algorithm outperforms the EM and MEM algorithms for the deterministic signal model. Furthermore, numerical results show that processing the same samples from the stochastic signal model, the SAGE algorithm for the deterministic signal model requires the fewest iterations.