# DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm

arXiv: https://arxiv.org/abs/2109.10581 · HTML: https://mlsanity.com/paper/2109.10581/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 68 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound](https://mlsanity.com/paper/1811.08252/) (2018)
- [Deep Networks for Direction-of-Arrival Estimation in Low SNR](https://mlsanity.com/paper/2011.08848/) (2020)
- [Broadband DOA estimation using Convolutional neural networks trained with noise signals](https://mlsanity.com/paper/1705.00919/) (2017)
- [DeepMUSIC: Multiple Signal Classification via Deep Learning](https://mlsanity.com/paper/1912.04357/) (2019)
- [Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals](https://mlsanity.com/paper/1807.11722/) (2018)
- [A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling](https://mlsanity.com/paper/2009.12858/) (2020)

## Built on by (most cited first, 10 of 10)

- [SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation](https://mlsanity.com/paper/2306.02271/) (2023)
- [Enhancing Off-Grid One-Bit DOA Estimation with Learning-Based Sparse Bayesian Approach for Non-Uniform Sparse Array](https://mlsanity.com/paper/2412.10976/) (2024)
- [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)
- [Autoregressive Attention Neural Networks for Non-Line-of-Sight User Tracking with Dynamic Metasurface Antennas](https://mlsanity.com/paper/2310.19767/) (2023)
- [DNN-based Methods of Jointly Sensing Number and Directions of Targets via a Green Massive H2AD MIMO Receiver](https://mlsanity.com/paper/2507.22906/) (2025)
- [Polarization based direction of arrival estimation using a radio interferometric array](https://mlsanity.com/paper/2510.15116/) (2025)
- [RF-LEGO: Modularized Signal Processing-Deep Learning Co-Design for RF Sensing via Deep Unrolling](https://mlsanity.com/paper/2604.10183/) (2026)
- [NeuralMUSIC: A Hybrid Neural-Subspace Framework for Robot Sound Source Localization](https://mlsanity.com/paper/2606.18664/) (2026)
- [Learning Array Signal Topologies as Conditional Neural Manifolds](https://mlsanity.com/paper/2609.18616/) (2026)

## Abstract (arXiv)

Direction of arrival (DoA) estimation of multiple signals is pivotal in sensor array signal processing. A popular multi-signal DoA estimation method is the multiple signal classification (MUSIC) algorithm, which enables high-performance super-resolution DoA recovery while being highly applicable in practice. MUSIC is a model-based algorithm, relying on an accurate mathematical description of the relationship between the signals and the measurements and assumptions on the signals themselves (non-coherent, narrowband sources). As such, it is sensitive to model imperfections. In this work we propose to overcome these limitations of MUSIC by augmenting the algorithm with specifically designed neural architectures. Our proposed deep augmented MUSIC (DA-MUSIC) algorithm is thus a hybrid model-based/data-driven DoA estimator, which leverages data to improve performance and robustness while preserving the interpretable flow of the classic method. DA-MUSIC is shown to learn to overcome limitations of the purely model-based method, such as its inability to successfully localize coherent sources as well as estimate the number of coherent signal sources present. We further demonstrate the superior resolution of the DA-MUSIC algorithm in synthetic narrowband and broadband scenarios as well as with real-world data of DoA estimation from seismic signals.