Multiple source localization using learning-based sparse estimation in deep ocean

2021 ◽  
Vol 150 (5) ◽  
pp. 3773-3786
Author(s):  
Yining Liu ◽  
Haiqiang Niu ◽  
Sisi Yang ◽  
Zhenglin Li
Keyword(s):  
Source Localization ◽  
Deep Ocean ◽  
Multiple Source ◽  
Sparse Estimation

UAV-based multiple source localization and contour mapping of radiation fields

2016 ◽  
Vol 85 ◽  
pp. 12-25 ◽  
Author(s):  
Abdullah Al Redwan Newaz ◽  
Sungmoon Jeong ◽  
Hosun Lee ◽  
Hyejeong Ryu ◽  
Nak Young Chong
Keyword(s):  
Source Localization ◽  
Multiple Source ◽  
Contour Mapping ◽  
Radiation Fields

scholarly journals Multiple Source Localization Based on Acoustic Map De-Emphasis

2010 ◽  
Vol 2010 ◽  
pp. 1-17 ◽  
Author(s):  
Alessio Brutti ◽  
Maurizio Omologo ◽  
Piergiorgio Svaizer
Keyword(s):  
Source Localization ◽  
Multiple Source

scholarly journals Multiple Source Localization in a Shallow Water Waveguide Exploiting Subarray Beamforming and Deep Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4768 ◽  
Author(s):  
Zhaoqiong Huang ◽  
Ji Xu ◽  
Zaixiao Gong ◽  
Haibin Wang ◽  
Yonghong Yan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Shallow Water ◽  
Source Localization ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Direction Finding ◽  
Multiple Source ◽  
Feed Forward Neural Network ◽  
Subarray Beamforming

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.

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