Abstract The classification of low signal-to-noise ratio (SNR) underwater acoustic signals in complex acoustic environments and increasingly small target radiation noise is a hot research topic.. This paper proposes a new method for signal processing—low SNR underwater acoustic signal classification method (LSUASC)—based on intrinsic modal features maintaining dimensionality reduction. Using the LSUASC method, the underwater acoustic signal was first transformed with the Hilbert-Huang Transform (HHT) and the intrinsic mode was extracted. the intrinsic mode was then transformed into a corresponding Mel-frequency cepstrum coefficient (MFCC) to form a multidimensional feature vector of the low SNR acoustic signal. Next, a semi-supervised fuzzy rough Laplacian Eigenmap (SSFRLE) method was proposed to perform manifold dimension reduction (local sparse and discrete features of underwater acoustic signals can be maintained in the dimension reduction process) and principal component analysis (PCA) was adopted in the process of dimension reduction to define the reduced dimension adaptively. Finally, Fuzzy C-Means (FCMs), which are able to classify data with weak features was adopted to cluster the signal features after dimensionality reduction. The experimental results presented here show that the LSUASC method is able to classify low SNR underwater acoustic signals with high accuracy.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A New Low SNR Underwater Acoustic Signal Classification Method Based on Intrinsic Modal Features Maintaining Dimensionality Reduction


    Beteiligte:
    Ju, Yang (Autor:in) / Wei, Zhengxian (Autor:in) / Huangfu, Li (Autor:in) / Xiao, Feng (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2020




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Unbekannt


    Klassifikation :

    BKL:    50.92 Meerestechnik / 55.40 Schiffstechnik, Schiffbau




    Nonlinear Dimensionality Reduction for Classification using Kernel Weighted Subspace Method

    Dai, G. / Yeung, D.-Y. | British Library Conference Proceedings | 2005



    Intrinsic Dimensionality as a Metric for Mission Design

    Hulley, Glynn / Miner, Kimberley / Townsend, Philip et al. | NTRS | 2021


    Spectral Signal Processing in Intrinsic Interferometric Sensors Based on Birefringent Polarization-Maintaining Optical Fibers

    Egorov, S. A. / Mamaev, A. N. / Polyantsev, A. S. | British Library Online Contents | 1995