# DeepAoANet: Learning Angle of Arrival from Software Defined Radios with Deep Neural Networks

arXiv: https://arxiv.org/abs/2112.00695 · HTML: https://mlsanity.com/paper/2112.00695/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 1 Dec 2021.

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

- [Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments](https://mlsanity.com/paper/1909.11800/) (2019)
- [Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network](https://mlsanity.com/paper/1710.10059/) (2017)
- [SoundDet: Polyphonic Moving Sound Event Detection and Localization from Raw Waveform](https://mlsanity.com/paper/2106.06969/) (2021)
- [Semi-supervised source localization with deep generative modeling](https://mlsanity.com/paper/2005.13163/) (2020)
- [Indoor Sound Source Localization with Probabilistic Neural Network](https://mlsanity.com/paper/1712.07814/) (2017)
- [Performance Advantages of Deep Neural Networks for Angle of Arrival Estimation](https://mlsanity.com/paper/1902.03569/) (2019)
- [Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning](https://mlsanity.com/paper/2004.11637/) (2020)
- [Direction of Arrival Estimation for a Vector Sensor Using Deep Neural Networks](https://mlsanity.com/paper/2004.05671/) (2020)

## Built on by (most cited first, 2 of 2)

- [SABER: Symbolic Regression-based Angle of Arrival and Beam Pattern Estimator](https://mlsanity.com/paper/2510.26340/) (2025)
- [Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios](https://mlsanity.com/paper/2607.02537/) (2026)

## Abstract (arXiv)

Direction finding and positioning systems based on RF signals are significantly impacted by multipath propagation, particularly in indoor environments. Existing algorithms (e.g MUSIC) perform poorly in resolving Angle of Arrival (AoA) in the presence of multipath or when operating in a weak signal regime. We note that digitally sampled RF frontends allow for the easy analysis of signals, and their delayed components. Low-cost Software-Defined Radio (SDR) modules enable Channel State Information (CSI) extraction across a wide spectrum, motivating the design of an enhanced Angle-of-Arrival (AoA) solution. We propose a Deep Learning approach to deriving AoA from a single snapshot of the SDR multichannel data. We compare and contrast deep-learning based angle classification and regression models, to estimate up to two AoAs accurately. We have implemented the inference engines on different platforms to extract AoAs in real-time, demonstrating the computational tractability of our approach. To demonstrate the utility of our approach we have collected IQ (In-phase and Quadrature components) samples from a four-element Universal Linear Array (ULA) in various Light-of-Sight (LOS) and Non-Line-of-Sight (NLOS) environments, and published the dataset. Our proposed method demonstrates excellent reliability in determining number of impinging signals and realized mean absolute AoA errors less than 2circ.