# SDOA-Net: An Efficient Deep Learning-Based DOA Estimation Network for Imperfect Array

arXiv: https://arxiv.org/abs/2203.10231 · HTML: https://mlsanity.com/paper/2203.10231/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2022.

## Built on by (most cited first, 5 of 5)

- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Multi-modal Iterative and Deep Fusion Frameworks for Enhanced Passive DOA Sensing via a Green Massive H2AD MIMO Receiver](https://mlsanity.com/paper/2411.06927/) (2024)
- [Model-driven deep neural network for enhanced direction finding with commodity 5G gNodeB](https://mlsanity.com/paper/2412.10644/) (2024)
- [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)

The estimation of direction of arrival (DOA) is a crucial issue in conventional radar, wireless communication, and integrated sensing and communication (ISAC) systems. However, low-cost systems often suffer from imperfect factors, such as antenna position perturbations, mutual coupling effect, inconsistent gains/phases, and non-linear amplifier effect, which can significantly degrade the performance of DOA estimation. This paper proposes a DOA estimation method named super-resolution DOA network (SDOA-Net) based on deep learning (DL) to characterize the realistic array more accurately. Unlike existing DL-based DOA methods, SDOA-Net uses sampled received signals instead of covariance matrices as input to extract data features. Furthermore, SDOA-Net produces a vector that is independent of the DOA of the targets but can be used to estimate their spatial spectrum. Consequently, the same training network can be applied to any number of targets, reducing the complexity of implementation. The proposed SDOA-Net with a low-dimension network structure also converges faster than existing DL-based methods. The simulation results demonstrate that SDOA-Net outperforms existing DOA estimation methods for imperfect arrays. The SDOA-Net code is available online at https://github.com/chenpengseu/SDOA-Net.git.