In:
The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 151, No. 6 ( 2022-06-01), p. 4140-4149
Abstract:
Underwater source localization by deep neural networks (DNNs) is challenging since training these DNNs generally requires a large amount of experimental data and is computationally expensive. In this paper, label distribution-guided transfer learning (LD-TL) for underwater source localization is proposed, where a one-dimensional convolutional neural network (1D-CNN) is pre-trained with the simulation data generated by an underwater acoustic propagation model and then fine-tuned with a very limited amount of experimental data. In particular, the experimental data for fine-tuning the pre-trained 1D-CNN are labeled with label distribution vectors instead of one-hot encoded vectors. Experimental results show that the performance of underwater source localization with a very limited amount of experimental data is significantly improved by the proposed LD-TL.
Type of Medium:
Online Resource
ISSN:
0001-4966
,
1520-8524
Language:
English
Publisher:
Acoustical Society of America (ASA)
Publication Date:
2022
detail.hit.zdb_id:
1461063-2
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