# Performance Advantages of Deep Neural Networks for Angle of Arrival Estimation

arXiv: https://arxiv.org/abs/1902.03569 · HTML: https://mlsanity.com/paper/1902.03569/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2019.

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

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [DeepAoANet: Learning Angle of Arrival from Software Defined Radios with Deep Neural Networks](https://mlsanity.com/paper/2112.00695/) (2021)
- [(SP)2-Net: A Neural Spatial Spectrum Method for DOA Estimation](https://mlsanity.com/paper/2509.15475/) (2025)
- [Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition](https://mlsanity.com/paper/2604.26793/) (2026)

## Abstract (arXiv)

The problem of estimating the number of sources and their angles of arrival from a single antenna array observation has been an active area of research in the signal processing community for the last few decades. When the number of sources is large, the maximum likelihood estimator is intractable due to its very high complexity, and therefore alternative signal processing methods have been developed with some performance loss. In this paper, we apply a deep neural network (DNN) approach to the problem and analyze its advantages with respect to signal processing algorithms. We show that an appropriate designed network can attain the maximum likelihood performance with feasible complexity and outperform other feasible signal processing estimation methods over various signal to noise ratios and array response inaccuracies.