DOA Estimation Method Based on Improved Deep Convolutional Neural Network

For the multi-target DOA estimation problem of uniform linear arrays, this paper proposes a DOA estimation method based on the deep convolution neural network. The algorithm adopts the deep convolutional neural network, and the DOA estimation problem of the array signal is transformed into the inver...

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Vydané v:Sensors (Basel, Switzerland) Ročník 22; číslo 4; s. 1305
Hlavní autori: Zhao, Fangzheng, Hu, Guoping, Zhan, Chenghong, Zhang, Yule
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 09.02.2022
MDPI
Predmet:
Algorithms
Computer Simulation
covariance matrix
deep convolutional neural network
Deep learning
DOA estimation
Methods
Neural networks
Neural Networks, Computer
Noise
Propagation
Signal processing
the upper triangular matrix
DOA estimation
deep convolutional neural network
the upper triangular matrix
covariance matrix
ISSN:1424-8220, 1424-8220
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Popis
Shrnutí:For the multi-target DOA estimation problem of uniform linear arrays, this paper proposes a DOA estimation method based on the deep convolution neural network. The algorithm adopts the deep convolutional neural network, and the DOA estimation problem of the array signal is transformed into the inverse mapping problem of the array output covariance matrix to a binary sequence in which “1” indicates that there is a target incident in the corresponding angular direction at that position. The upper triangular array of the discrete covariance matrix is used as the data input to realize the DOA estimation of multiple sources. The simulation results show that the DOA estimation accuracy of the proposed algorithm is significantly better than that of the typical super-resolution estimation algorithm under the conditions of low SNR and small snapshot. Under the conditions of high SNR and large snapshot, the estimation accuracy of the proposed algorithm is basically the same as those of the MUSIC algorithm, ESPRIT algorithm, and ML algorithm, which are better than that of the deep fully connected neural network. The analysis of the simulation results shows that the algorithm is effective, and the time and space complexity can be further reduced by replacing the square array with the upper triangular array as the input.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22041305