ArticleSensors (Basel, Switzerland)2020
A-CRNN-Based Method for Coherent DOA Estimation with Unknown Source Number.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Enhancing Direction-of-Arrival Estimation with Multi-Task Learning.Sensors (Basel, Switzerland) · 2024Article
- Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array.Sensors (Basel, Switzerland) · 2023Article
- Small Sample Coherent DOA Estimation Method Based on S2S Neural Network Meta Reinforcement Learning.Sensors (Basel, Switzerland) · 2023Article
- Coherent Signal DOA Estimation for MIMO Radar under Composite Background of Strong Interference and Non-Uniform Noise.Sensors (Basel, Switzerland) · 2022Article
- The DOA Estimation Method for Low-Altitude Targets under the Background of Impulse Noise.Sensors (Basel, Switzerland) · 2022Article
- 2D-DOA Estimation in Switching UCA Using Deep Learning-Based Covariance Matrix Completion.Sensors (Basel, Switzerland) · 2022Article
- Off-Grid DOA Estimation Based on Circularly Fully Convolutional Networks (CFCN) Using Space-Frequency Pseudo-Spectrum.Sensors (Basel, Switzerland) · 2021Article
- Underwater Ambiguity Elimination Method Based on Co-Prime Sensor Array.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
3 authors.
Funding
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Abstract
Estimating directions of arrival (DOA) without knowledge of the source number is regarded as a challenging task, particularly when coherence among sources exists. Researchers have trained deep learning (DL)-based models to attack the problem of DOA estimation. However, existing DL-based methods for coherent sources do not adapt to variable source numbers or require signal independence. Herein, we put forward a new framework combining parallel DOA estimators with Toeplitz matrix reconstruction to address the problem. Each estimator is constructed by connecting a multi-label classifier to a spatial filter, which is based on convolutional-recurrent neural networks. Spatial filters divide the angle domain into several sectors, so that the following classifiers can extract the arrival directions. Assisted with Toeplitz-based method for source-number determination, pseudo or missed angles classified by the estimators will be reduced. Then, the spatial spectrum can be more accurately recovered. In addition, the proposed method is data-driven, so it is naturally immune to signal coherence. Simulation results demonstrate the predominance of the proposed method and show that the trained model is robust to imperfect circumstances such as limited snapshots, colored Gaussian noise, and array imperfections.
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