Joint color spectrum and conditional generative adversarial network processing for underwater acoustic source ranging

In recent years, machine learning has been prospectively performed in underwater acoustic ranging task. The scale of acoustic observation has a considerable impact in the performance of networks. In this paper, we propose a joint color spectrum and conditional generative adversarial network processing for underwater acoustic source ranging. The joint method maps acoustic observations into color images to extract features. Original images are fed into conditional generative adversarial network and vast images generated from the generator network could be regarded as an expansion of the training set. The feasibility and effectiveness of the joint method are verified through some statistical indexes. In addition, variational auto-encoder and principal component analysis are used to measure the similarity of generated and original data. The method yields a meaningful improvement for underwater acoustic source ranging. (C) 2021 Elsevier Ltd. All rights reserved.