# Advancing Single-Snapshot DOA Estimation with Siamese Neural Networks for Sparse Linear Arrays

arXiv: https://arxiv.org/abs/2501.07008 · HTML: https://mlsanity.com/paper/2501.07008/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 13 Jan 2025.

## Builds on (most cited first, 3 of 3)

- [Deep Networks for Direction-of-Arrival Estimation in Low SNR](https://mlsanity.com/paper/2011.08848/) (2020)
- [Neural Network-Based DOA Estimation in the Presence of Non-Gaussian Interference](https://mlsanity.com/paper/2301.02856/) (2023)
- [Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion](https://mlsanity.com/paper/2312.05423/) (2023)

## 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)

Single-snapshot signal processing in sparse linear arrays has become increasingly vital, particularly in dynamic environments like automotive radar systems, where only limited snapshots are available. These arrays are often utilized either to cut manufacturing costs or result from unintended antenna failures, leading to challenges such as high sidelobe levels and compromised accuracy in direction-of-arrival (DOA) estimation. Despite deep learning's success in tasks such as DOA estimation, the need for extensive training data to increase target numbers or improve angular resolution poses significant challenges. In response, this paper presents a novel Siamese neural network (SNN) featuring a sparse augmentation layer, which enhances signal feature embedding and DOA estimation accuracy in sparse arrays. We demonstrate the enhanced DOA estimation performance of our approach through detailed feature analysis and performance evaluation. The code for this study is available at https://github.com/ruxinzh/SNNS_SLA.