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

arXiv: https://arxiv.org/abs/2309.08174 · HTML: https://mlsanity.com/paper/2309.08174/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2023.

## Built on by (most cited first, 4 of 4)

- [Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays](https://mlsanity.com/paper/2411.15144/) (2024)
- [Near Field Localization via AI-Aided Subspace Methods](https://mlsanity.com/paper/2504.00599/) (2025)
- [Acoustic Imaging for UAV Detection: Dense Beamformed Energy Maps and U-Net SELD](https://mlsanity.com/paper/2508.00307/) (2025)
- [Reliable Near-Field Multi-User Positioning Informed by Two-Stage MUSIC](https://mlsanity.com/paper/2609.09409/) (2026)

## Abstract (arXiv)

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.