# Deep Networks for Direction-of-Arrival Estimation in Low SNR

arXiv: https://arxiv.org/abs/2011.08848 · HTML: https://mlsanity.com/paper/2011.08848/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 20 of 20)

- [DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm](https://mlsanity.com/paper/2109.10581/) (2021)
- [SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation](https://mlsanity.com/paper/2306.02271/) (2023)
- [Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays](https://mlsanity.com/paper/2411.15144/) (2024)
- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Attention-Based Fusion of IQ and FFT Spectrograms with AoA Features for GNSS Jammer Localization](https://mlsanity.com/paper/2507.14167/) (2025)
- [Co-learning-aided Multi-modal-deep-learning Framework of Passive DOA Estimators for a Heterogeneous Hybrid Massive MIMO Receiver](https://mlsanity.com/paper/2405.09556/) (2024)
- [Subspace Representation Learning for Sparse Linear Arrays to Localize More Sources than Sensors: A Deep Learning Methodology](https://mlsanity.com/paper/2408.16605/) (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)
- [Near Field Localization via AI-Aided Subspace Methods](https://mlsanity.com/paper/2504.00599/) (2025)
- [GNSS Jammer Direction Finding in Dynamic Scenarios Using an Inertial-based Multi-Antenna System](https://mlsanity.com/paper/2512.05128/) (2025)
- [A Deep Learning Approach Utilizing Covariance Matrix Analysis for the ISBI Edited MRS Reconstruction Challenge](https://mlsanity.com/paper/2306.02984/) (2023)
- [Super-Resolution Radar Imaging with Sparse Arrays Using a Deep Neural Network Trained with Enhanced Virtual Data](https://mlsanity.com/paper/2306.09839/) (2023)
- [A Comparative Study of Invariance-Aware Loss Functions for Deep Learning-based Gridless Direction-of-Arrival Estimation](https://mlsanity.com/paper/2503.12386/) (2025)
- [Multi-Branch DNN and CRLB-Ratio-Weight Fusion for Enhanced DOA Sensing via a Massive H2AD MIMO Receiver](https://mlsanity.com/paper/2506.23203/) (2025)
- [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)
- [Learning Array Signal Topologies as Conditional Neural Manifolds](https://mlsanity.com/paper/2609.18616/) (2026)
- [CNN-Transformer with Sigmoid Label for High-Resolution Sub-Degree DOA Estimation](https://mlsanity.com/paper/2610.13433/) (2026)
- [Comparative Evaluation of Deep-Learning based DoA Estimators for Real-Time ISAC Beamforming](https://mlsanity.com/paper/2610.20326/) (2026)

## Abstract (arXiv)

In this work, we consider direction-of-arrival (DoA) estimation in the presence of extreme noise using Deep Learning (DL). In particular, we introduce a Convolutional Neural Network (CNN) that is trained from mutli-channel data of the true array manifold matrix and is able to predict angular directions using the sample covariance estimate. We model the problem as a multi-label classification task and train a CNN in the low-SNR regime to predict DoAs across all SNRs. The proposed architecture demonstrates enhanced robustness in the presence of noise, and resilience to a small number of snapshots. Moreover, it is able to resolve angles within the grid resolution. Experimental results demonstrate significant performance gains in the low-SNR regime compared to state-of-the-art methods and without the requirement of any parameter tuning. We relax the assumption that the number of sources is known a priori and present a training method, where the CNN learns to infer the number of sources jointly with the DoAs. Simulation results demonstrate that the proposed CNN can accurately estimate off-grid angles in low SNR, while at the same time the number of sources is successfully inferred for a sufficient number of snapshots. Our robust solution can be applied in several fields, ranging from wireless array sensors to acoustic microphones or sonars.