Abstract:
This article presents an underwater acoustic target recognition method to reduce recognition errors in continuous recordings caused by variations in ship operating condit...Show MoreMetadata
Abstract:
This article presents an underwater acoustic target recognition method to reduce recognition errors in continuous recordings caused by variations in ship operating conditions. The proposed method comprises two stages: spectral feature classification and supervised contrastive learning, and it is called SFC-Sup as a result in this article. In the first stage, a new spectral feature classification strategy is designed to choose appropriate feature sets for contrastive learning, based on which an instance discrimination pretext task is created by utilizing different spectral features to capture invariant features across segments under different operating conditions. In the second stage, a dynamic weighted loss function is introduced to guide the joint optimization process in the framework of contrastive learning. Different from the existing methods that focus on improving recognition accuracy by designing features for individual segments, the proposed two-stage method SFC-Sup considers consistent features across diverse segments, which is expected to improve recognition accuracy in a continuous recording. Experimental results demonstrate that, in the presence of complex operating conditions, SFC-Sup exhibits superior stability and enhances recognition accuracy by 2.06% compared to state-of-the-art methods.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 61)