NDLI: Searching over DOA parameter space via neural networks

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Searching over DOA parameter space via neural networks

Content Provider	IEEE Xplore Digital Library
Author	Sheng Lin Qin-Ye Yin
Copyright Year	1994
Description	Author affiliation: Dept. of Inf. & Control Eng., Xi'an Jiaotong Univ., China (Sheng Lin; Qin-Ye Yin)
Abstract	In this paper, we propose a neural method to solve the orthogonality search problem arising in direction-of-arrival (DOA) estimation. The most important feature of this method hinges upon the fact that it can offer the potential of real-time solutions to the above problem by utilizing the fast relaxation properties of the Hopfield's linear programming neural network. Theoretical analysis and simulation results show that the performance of neural method is exactly equivalent to that of the standard MUSIC method or the Real Domain DOA estimation method (RD method). That is to say, the method proposed in this paper is a neural implementation of the MUSIC method and RD method.<>
Starting Page	295
Ending Page	298
File Size	259981
Page Count	4
File Format	PDF
ISBN	078031915X
DOI	10.1109/ISCAS.1994.409584
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-05-30
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Direction of arrival estimation Multiple signal classification Hopfield neural networks Computational efficiency Linear programming Analytical models Signal resolution Signal processing Parameter estimation
Content Type	Text
Resource Type	Article

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