An Intelligent Direction Finding Method with Deep Neural Network

Different from the model-driven direction finding (DF) methods, the data-driven DF methods have many advantages, such as not relying on the array geometry, no need for a special channel calibration module, and better adaptability to the DF system error. In this paper, an intelligent DF method based...

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Bibliographic Details
Published in2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT pp. 1094 - 1098
Main Authors Xu, Yajun, Guo, Hesong, Fan, Rong, Si, Chengke, Ding, Xueke
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.10.2020
Subjects
array signal processing
deep neural network
Direction-of-arrival estimation
Estimation
Feature extraction
intelligent direction finding
Neural networks
Residual neural networks
Signal to noise ratio
Simulation
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Summary:Different from the model-driven direction finding (DF) methods, the data-driven DF methods have many advantages, such as not relying on the array geometry, no need for a special channel calibration module, and better adaptability to the DF system error. In this paper, an intelligent DF method based on deep neural network (DNN) is developed. It is composed of auto-encoder network and residual neural network. Numerical simulation experiments have demonstrated the superiority of the proposed method in direction of arrival (DOA) estimation precision especially when the signal-to-noise ratio (SNR) is low.
DOI:10.1109/ICCASIT50869.2020.9368549