TransMUSIC: A Transformer-Aided Subspace Method for DOA Estimation with Low-Resolution ADCs

TL;DR

TransMUSIC introduces a Transformer-assisted subspace method for accurate DOA estimation using low-resolution ADCs, effectively handling quantization distortion and enabling gridless, source number estimation.

Contribution

It presents a novel Transformer-based framework that improves subspace estimation and source counting in low-resolution quantized data for DOA estimation.

Findings

Outperforms existing methods with one-bit data

Accurately estimates the number of sources

Effective in large-scale array scenarios

Abstract

Direction of arrival (DOA) estimation employing low-resolution analog-to-digital convertors (ADCs) has emerged as a challenging and intriguing problem, particularly with the rise in popularity of large-scale arrays. The substantial quantization distortion complicates the extraction of signal and noise subspaces from the quantized data. To address this issue, this paper introduces a novel approach that leverages the Transformer model to aid the subspace estimation. In this model, multiple snapshots are processed in parallel, enabling the capture of global correlations that span them. The learned subspace empowers us to construct the MUSIC spectrum and perform gridless DOA estimation using a neural network-based peak finder. Additionally, the acquired subspace encodes the vital information of model order, allowing us to determine the exact number of sources. These integrated components form a unified algorithmic framework referred to as TransMUSIC. Numerical results demonstrate the superiority of the TransMUSIC algorithm, even when dealing with one-bit quantized data. The results highlight the potential of Transformer-based techniques in DOA estimation.