Impact of Model Mismatch on DOA Estimation with MUSIC: Near-Field and Far-Field

TL;DR

This paper investigates how model mismatch affects the performance of the MUSIC algorithm in DOA estimation across near-field and far-field regimes, highlighting the importance of using appropriate models for accurate results.

Contribution

It quantifies the performance loss due to model mismatch in near-field and far-field scenarios and demonstrates MUSIC's capability for accurate range estimation within the Fraunhofer distance.

Findings

Performance drops when using incorrect models in near-field.

Underestimating DOA error with far-field assumptions in near-field.

Accurate range estimation within the Fraunhofer distance.

Abstract

There has been substantial work on developing variants of the multiple signal classification (MUSIC) algorithms that take advantage of the information present in the near-field propagation regime. However, it is not always easy to determine the correct propagation regime, which opens the possibility of incorrectly applying simpler algorithms (meant for far-field) in the near-field regime. Inspired by this, we use simulation results to investigate the performance drop when there is a mismatch between the signal model in the MUSIC algorithm and the propagation regime. For direction of arrival (DOA) estimation, we consider the cases when the receiver is in the near-field region but uses i) the near-field model, ii) the approximate near-field model (ANM) model, and iii) the far-field model to design the beamforming matrix in the MUSIC algorithm. We also consider the case when the receiver is in the far-field region, and we use the correct far-field model to design the beamforming matrix in the MUSIC algorithm. One contribution is that in the near-field, we have quantified the loss in performance when the ANM and the far-field model are used to create the beamforming matrix for the MUSIC algorithm, causing a reduction in estimation accuracy compared to the case when the correct near-field model is used to design the beamforming matrix. Another result is that in the near-field, when we incorrectly assume that the receiver is in the far-field and subsequently use the far-field beamforming matrix, we underestimate the DOA estimation error. Finally, we show that the MUSIC algorithm can provide very accurate range estimates for distances less than the Fraunhofer distance. This estimate gradually becomes inaccurate as the distances exceed the Fraunhofer distance.