Title
Multiple Source Localization in a Shallow Water Waveguide Exploiting Subarray Beamforming and Deep Neural Networks.
Abstract
Deep neural networks (DNNs) have been shown to be effective for single sound source localization in shallow water environments. However, multiple source localization is a more challenging task because of the interactions among multiple acoustic signals. This paper proposes a framework for multiple source localization on underwater horizontal arrays using deep neural networks. The two-stage DNNs are adopted to determine both the directions and ranges of multiple sources successively. A feed-forward neural network is trained for direction finding, while the long short term memory recurrent neural network is used for source ranging. Particularly, in the source ranging stage, we perform subarray beamforming to extract features of sources that are detected by the direction finding stage, because subarray beamforming can enhance the mixed signal to the desired direction while preserving the horizontal-longitudinal correlations of the acoustic field. In this way, a universal model trained in the single-source scenario can be applied to multi-source scenarios with arbitrary numbers of sources. Both simulations and experiments in a range-independent shallow water environment of SWellEx-96 Event S5 are given to demonstrate the effectiveness of the proposed method.
Year
DOI
Venue
2019
10.3390/s19214768
SENSORS
Keywords
Field
DocType
multiple source localization,deep neural network,subarray beamforming,shallow water environment
Direction finding,Beamforming,Pattern recognition,Recurrent neural network,Electronic engineering,Ranging,Artificial intelligence,Engineering,Mixed-signal integrated circuit,Artificial neural network,Acoustic source localization,Underwater
Journal
Volume
Issue
ISSN
19
21
1424-8220
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Zhaoqiong Huang101.35
Ji Xu234.14
Zaixiao Gong300.34
H. Wang48415.66
Yonghong Yan5656114.13