Interpretable features for underwater acoustic target recognition. (March 2021)

• Record Type: Journal Article
• Authors: Jiang, Junjun; Wu, Zhenning; Lu, Junan; Huang, Min; Xiao, Zhongzhe
• Is Part Of: Measurement. Volume 173(2021)
• Publication Date: 2021-03
• Subjects: Interpretable features -- Underwater acoustic target -- Detection and ranging -- Machine learning; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.108586
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: The major challenge of underwater acoustic target recognition is that the features clearly characterizing the underwater acoustic targets remain indistinct, where the sound signals are often submerged by intense noise. In this paper, we aim to discover an efficient interpretable feature set that can reveal the inherent mechanism, and result into eighty-eight efficient features. The performance of these features is evaluated with experiments by BP Neural Network. CNN is used as the baseline method. The accuracy of detection based on these features can reach 87.22%, 99.31%, and 91.56% in three sea areas, while CNN shows the accuracy of 64.17%, 99.17%, and 59.17%, respectively. The lowest MRE of ranging with these features collaborating with BP Neural Network is only 7.09%. The experimental results show that these features indeed exist validity for underwater acoustic target recognition with explicit physical interpretability, and lead to very low computational complexity in recognition. Highlights: The shallow and essential characteristics of target are analyzed in detail. A set of clear features are found applicable to underwater acoustic signal. These features carry explicit physical meanings with low computational complexity. Underwater acoustic target can be recognized effectively based on these features. Better performance with traditional machine learning algorithms than deep ones.