A-CRNN-Based Method for Coherent DOA Estimation with Unknown Source Number

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-04, Vol.20 (8), p.2296
Main Authors: Yao, Yuanyuan, Lei, Hong, He, Wenjing
Format: Article
Language:English
Subjects:
Algorithms
Classification
Coherence
colored Gaussian noise, coherent sources
Computer simulation
convolutional-recurrent neural network
Decomposition
Direction of arrival
direction-of-arrival (DOA) estimation
Music
Neural networks
Noise
Random noise
Recurrent neural networks
Signal processing
Spatial filtering
Toeplitz matrix reconstruction
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20082296