Efficient Multi-Source Localization in Near-Field Using only Angular Domain MUSIC

TL;DR

This paper introduces a novel angular domain MUSIC-based method for near-field multi-source localization that significantly reduces computational complexity while maintaining high accuracy, outperforming existing modified algorithms.

Contribution

A new angular variation-based approach that localizes near-field sources efficiently without extensive grid searches, reducing complexity by over 370 times.

Findings

Outperforms Modified MUSIC in mean absolute error

Achieves accuracy comparable to standard MUSIC

Reduces computational complexity drastically

Abstract

The localization of multiple signal sources using sensor arrays has been a long-standing research challenge. While numerous solutions have been developed, signal space methods like MUSIC and ESPRIT have gained widespread popularity. As sensor arrays grow in size, sources are frequently located in the near-field region. The standard MUSIC algorithm can be adapted to locate these sources by performing a 3D search over both the distance and the angles of arrival (AOA), including azimuth and elevation, though this comes with significant computational complexity. To address this, a modified version of MUSIC has been developed to decouple the AoA and distance, enabling sequential estimation of these parameters and reducing computational demands. However, this approach suffers from reduced accuracy. To maintain the accuracy of MUSIC while minimizing complexity, this paper proposes a novel method that exploits angular variation across the array aperture, eliminating the need for a grid search over distance. The proposed method divides the large aperture into smaller sections, with each focusing on estimating the angles of arrival. These angles are then triangulated to localize the sources in the near-field of the large aperture. Numerical simulations show that this approach not only surpasses the Modified MUSIC algorithm in terms of mean absolute error but also achieves accuracy comparable to standard MUSIC, all while greatly reducing computational complexity-370 times in our simulation scenario.