Maximum Likelihood and Maximum A Posteriori Direction-of-Arrival Estimation in the Presence of SIRP Noise

TL;DR

This paper develops iterative ML and MAP algorithms for DOA estimation in sensor arrays when noise follows a non-Gaussian SIRP model, improving accuracy over traditional Gaussian-based methods.

Contribution

It introduces novel iterative ML and MAP algorithms tailored for SIRP noise, extending DOA estimation techniques to more realistic non-Gaussian noise environments.

Findings

Proposed algorithms outperform conventional ML in non-Gaussian noise conditions.

Algorithms demonstrate robustness and improved accuracy in simulations.

SIRP noise modeling enhances DOA estimation in radar applications.

Abstract

The maximum likelihood (ML) and maximum a posteriori (MAP) estimation techniques are widely used to address the direction-of-arrival (DOA) estimation problems, an important topic in sensor array processing. Conventionally the ML estimators in the DOA estimation context assume the sensor noise to follow a Gaussian distribution. In real-life application, however, this assumption is sometimes not valid, and it is often more accurate to model the noise as a non-Gaussian process. In this paper we derive an iterative ML as well as an iterative MAP estimation algorithm for the DOA estimation problem under the spherically invariant random process noise assumption, one of the most popular non-Gaussian models, especially in the radar context. Numerical simulation results are provided to assess our proposed algorithms and to show their advantage in terms of performance over the conventional ML algorithm.