Moment-Matching Array Processing Technique for diffuse source estimation

TL;DR

This paper presents MoMET, a low-complexity, robust method for estimating the mean direction, spread, and power of diffuse sources in signal processing without relying on prior distribution assumptions.

Contribution

The paper introduces MoMET, a novel moment-matching technique that accurately estimates diffuse source parameters without prior knowledge, reducing bias and computational complexity.

Findings

MoMET accurately estimates source parameters in simulations.

The method is robust to incorrect model assumptions.

Asymptotic bias and covariance are analytically derived.

Abstract

Direction of Arrival (DOA) estimation is a fundamental problem in signal processing. Diffuse sources, whose power density cannot be represented with a single angular coordinate, are usually characterized based on prior assumptions, which associate the source angular density with a specific set of functions. However, these assumptions can lead to significant estimation biases when they are incorrect. This paper introduces the Moment-Matching Estimation Technique (MoMET), a low-complexity method for estimating the mean DOA, spread, and power of a narrow diffuse source without requiring prior knowledge on the source distribution. The unknown source density is characterized by its mean DOA and its first central moments, which are estimated through covariance matching techniques which fit the empirical covariance of the measurements to that modeled from the moments. The MoMET parameterization is robust to incorrect model assumptions, and numerically efficient. The asymptotic bias and covariance of the new estimator are derived and its performance is demonstrated through simulations.