SiML: Sieved Maximum Likelihood for Array Signal Processing

TL;DR

This paper introduces SiML, a novel array signal processing method that improves upon traditional SML by allowing for more flexible source models, reducing computational costs, and enhancing accuracy in direction of arrival estimation.

Contribution

The paper proposes SiML, a new functional data model-based approach that extends SML to handle arbitrarily-shaped sources with better efficiency and robustness.

Findings

SiML is computationally more efficient than traditional SML.

SiML achieves higher accuracy than spectral-based methods.

SiML is resilient to noise in DOA estimation.

Abstract

Stochastic Maximum Likelihood (SML) is a popular direction of arrival (DOA) estimation technique in array signal processing. It is a parametric method that jointly estimates signal and instrument noise by maximum likelihood, achieving excellent statistical performance. Some drawbacks are the computational overhead as well as the limitation to a point-source data model with fewer sources than sensors. In this work, we propose a Sieved Maximum Likelihood (SiML) method. It uses a general functional data model, allowing an unrestricted number of arbitrarily-shaped sources to be recovered. To this end, we leverage functional analysis tools and express the data in terms of an infinite-dimensional sampling operator acting on a Gaussian random function. We show that SiML is computationally more efficient than traditional SML, resilient to noise, and results in much better accuracy than spectral-based methods.