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中文核心期刊

基于小波域多尺度统计建模的水下噪声的检测与识别

Detection and recognition research of underwater noise based on multi-scale statistical model in wavelet domain

  • 摘要: 研究了基于小波域多尺度树结构的水下噪声信号统计概率模型的建模方法。实验分析表明,船舶辐射噪声的模型特征不仪与海洋环境噪声的有很大差异,而且在不同工况下自身也表现出了不同特点。根据海洋环境噪声和船舶辐射噪声在模型参数上的特征差异,提出了一种从海洋环境噪声中检测船舶辐射噪声的方法,实验证明了该方法的检测性能大大优于以前提出的几种检测方法。另外,为了更好地解决船舶辐射噪声的分类问题,在研究了基于隐马尔可夫统计模型的分类方法的基础上,还提出结合支撑向量机和隐马尔可夫模型的综合分类方法,实验分析也取得了较好的结果。

     

    Abstract: The statistics modeling method of underwater noise signal is studied. The hidden Markov tree model in wavelet domain is adopted to model the sea noise and various ship-radiated noise. Using HMT models, properties of HMT model for ship-radiated noise are thoroughly studied by experiments in different work conditions and sea conditions. The results can instruct to set appropriate values for parameters of the HMT model. Moreover, using difference between models of the sea noise and that of the ship-radiated noise, a method of detecting ship-radiated noise from sea noise is put forward. Experiments proved that the method outperform methods which are based high order statistics analysis, zero-crossing detection and energy detection respectively. Furthermore, an improved classification approach based on HMT model is presented, which integrates the wavelet coefficients HMT models with support vector machine. The performance of this approach is evaluated experimentally in classifying of four types of acoustic noises. With an accuracy of more than 90%, this HMT-based approach is found to outperform previously proposed classifiers.

     

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