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A Novel Phase Enhancement Method for Low-Angle Estimation Based on Supervised DNN Learning
- Source :
- IEEE Access, Vol 7, Pp 82329-82336 (2019)
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- In low-altitude target situation, the multi-path signals cause the amplitude-phase distortion of direct signal from targets and degrade the performance of existing methods. Hence, in this paper, we propose a phase enhancement method for low-angle estimation using supervised deep neural network (DNN) to mitigate the phase distortion, thus to improve direction of arrival (DOA) estimation accuracy. The mapping relationship between the original phase difference distribution of the received signal and desired phase difference distribution is learned by DNN during training. The phase of test data is enhanced by trained DNN, and the enhanced phase is used for DOA estimation. We explain the significance of enhancing phase instead of amplitude by discussing the sensitivity of amplitude and phase on DOA estimation. Moreover, we prove the effectiveness and superiority of the proposed method by simulation experiments. The results demonstrate that the proposed technique has a better performance in terms of estimation error and goodness of fit (GoF) than the physics-driven DOA estimation methods and state-of-the-art methods including feature reversal and the support vector regression (SVR).
- Subjects :
- General Computer Science
Artificial neural network
business.industry
Computer science
Phase distortion
General Engineering
Phase (waves)
Phase enhancement
Direction of arrival
020206 networking & telecommunications
Pattern recognition
DOA estimation
02 engineering and technology
Amplitude
Distortion
supervised deep neural network
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
General Materials Science
Artificial intelligence
Sensitivity (control systems)
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
Test data
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....4cdfa7b40ce9477f0b86ee17048c5a45