Neural Network-Based DOA Estimation in the Presence of Non-Gaussian Interference

TL;DR

This paper introduces a neural network-based method for accurate direction-of-arrival estimation in challenging environments with non-Gaussian, spatially-colored interference, outperforming traditional techniques especially under low SIR and limited data conditions.

Contribution

It proposes a novel neural network approach that jointly estimates the number of sources and their DOAs in complex interference scenarios, improving over existing methods.

Findings

Significantly better resolution probability than conventional methods.

Higher estimation accuracy in low SIR environments.

Effective source enumeration with limited samples.

Abstract

This work addresses the problem of direction-of-arrival (DOA) estimation in the presence of non-Gaussian, heavy-tailed, and spatially-colored interference. Conventionally, the interference is considered to be Gaussian-distributed and spatially white. However, in practice, this assumption is not guaranteed, which results in degraded DOA estimation performance. Maximum likelihood DOA estimation in the presence of non-Gaussian and spatially colored interference is computationally complex and not practical. Therefore, this work proposes a neural network (NN) based DOA estimation approach for spatial spectrum estimation in multi-source scenarios with a-priori unknown number of sources in the presence of non-Gaussian spatially-colored interference. The proposed approach utilizes a single NN instance for simultaneous source enumeration and DOA estimation. It is shown via simulations that the proposed approach significantly outperforms conventional and NN-based approaches in terms of probability of resolution, estimation accuracy, and source enumeration accuracy in conditions of low SIR, small sample support, and when the angular separation between the source DOAs and the spatially-colored interference is small.