Source localization in the deep ocean using a convolutional neural network
Wenxu Liu, Yixin Yang, Mengqian Xu, Lian‐Gang Lü, Zongwei Liu, Yang Shi
Abstract
In deep-sea source localization, some of the existing methods only estimate the source range, while the others produce large errors in distance estimation when estimating both the range and depth. Here, a convolutional neural network-based method with high accuracy is introduced, in which the source localization problem is solved as a regression problem. The proposed neural network is trained by a normalized acoustic matrix and used to predict the source position. Experimental data from the western Pacific indicate that this method performs satisfactorily: the mean absolute percentage error of the range is 2.10%, while that of the depth is 3.08%.
Topics & Concepts
Convolutional neural networkRange (aeronautics)Position (finance)Computer scienceArtificial neural networkArtificial intelligenceDeep neural networksPattern recognition (psychology)Matrix (chemical analysis)AlgorithmGeodesyGeologyEngineeringComposite materialEconomicsMaterials scienceAerospace engineeringFinanceUnderwater Acoustics ResearchGeophysical Methods and ApplicationsUnderwater Vehicles and Communication Systems