# A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling

arXiv: https://arxiv.org/abs/2009.12858 · HTML: https://mlsanity.com/paper/2009.12858/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2020.

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

- [DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm](https://mlsanity.com/paper/2109.10581/) (2021)
- [Subspace Representation Learning for Sparse Linear Arrays to Localize More Sources than Sensors: A Deep Learning Methodology](https://mlsanity.com/paper/2408.16605/) (2024)
- [Audio scene monitoring using redundant ad-hoc microphone array networks](https://mlsanity.com/paper/2103.01830/) (2021)
- [Model-driven deep neural network for enhanced direction finding with commodity 5G gNodeB](https://mlsanity.com/paper/2412.10644/) (2024)

## Abstract (arXiv)

In this paper, we study the problem of direction of arrival estimation and model order selection for systems employing subarray sampling. Thereby, we focus on scenarios, where the number of active sources is not smaller than the number of simultaneously sampled antenna elements. For this purpose, we propose new schemes based on neural networks and estimators that combine neural networks with gradient steps on the likelihood function. These methods are able to outperform existing estimators in terms of mean squared error and model selection accuracy, especially in the low snapshot domain, at a drastically lower computational complexity.