# Enhancing Off-Grid One-Bit DOA Estimation with Learning-Based Sparse Bayesian Approach for Non-Uniform Sparse Array

arXiv: https://arxiv.org/abs/2412.10976 · HTML: https://mlsanity.com/paper/2412.10976/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 14 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm](https://mlsanity.com/paper/2109.10581/) (2021)
- [One-Bit MUSIC](https://mlsanity.com/paper/1901.05109/) (2019)
- [On the Performance of One-Bit DoA Estimation via Sparse Linear Arrays](https://mlsanity.com/paper/2012.14051/) (2020)
- [Sinusoidal Parameter Estimation from Signed Measurements via Majorization-Minimization Based RELAX](https://mlsanity.com/paper/2103.11500/) (2021)
- [Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation](https://mlsanity.com/paper/2309.07411/) (2023)
- [Weighted SPICE Algorithms for Range-Doppler Imaging Using One-Bit Automotive Radar](https://mlsanity.com/paper/2103.17032/) (2021)

## Built on by (most cited first, 1 of 1)

- [Model-Based Learning for DOA Estimation with One-Bit Single-Snapshot Sparse Arrays](https://mlsanity.com/paper/2502.17473/) (2025)

## Abstract (arXiv)

This paper tackles the challenge of one-bit off-grid direction of arrival (DOA) estimation in a single snapshot scenario based on a learning-based Bayesian approach. Firstly, we formulate the off-grid DOA estimation model, utilizing the first-order off-grid approximation, incorporating one-bit data quantization. Subsequently, we address this problem using the Sparse Bayesian based framework and solve iteratively. However, traditional Sparse Bayesian methods often face challenges such as high computational complexity and the need for extensive hyperparameter tuning. To balance estimation accuracy and computational efficiency, we propose a novel Learning-based Sparse Bayesian framework, which leverages an unrolled neural network architecture. This framework autonomously learns hyperparameters through supervised learning, offering more accurate off-grid DOA estimates and improved computational efficiency compared to some state-of-the-art methods. Furthermore, the proposed approach is applicable to both uniform linear arrays and non-uniform sparse arrays. Simulation results validate the effectiveness of the proposed framework.