# Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion

arXiv: https://arxiv.org/abs/2312.05423 · HTML: https://mlsanity.com/paper/2312.05423/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2023.

## Built on by (most cited first, 3 of 3)

- [Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion](https://mlsanity.com/paper/2403.08168/) (2024)
- [Advancing Single-Snapshot DOA Estimation with Siamese Neural Networks for Sparse Linear Arrays](https://mlsanity.com/paper/2501.07008/) (2025)
- [Model-Based Learning for DOA Estimation with One-Bit Single-Snapshot Sparse Arrays](https://mlsanity.com/paper/2502.17473/) (2025)

## Abstract (arXiv)

The design of sparse linear arrays has proven instrumental in the implementation of cost-effective and efficient automotive radar systems for high-resolution imaging. This paper investigates the impact of coarse quantization on measurements obtained from such arrays. To recover azimuth angles from quantized measurements, we leverage the low-rank properties of the constructed Hankel matrix. In particular, by addressing the one-bit Hankel matrix completion problem through a developed singular value thresholding algorithm, our proposed approach accurately estimates the azimuth angles of interest. We provide comprehensive insights into recovery performance and the required number of one-bit samples. The effectiveness of our proposed scheme is underscored by numerical results, demonstrating successful reconstruction using only one-bit data.