水下目标的Gammatone子带降噪和希尔伯特-黄变换特征提取

Gammatone Subband Denoising and HHT-based Feature Extraction for Underwater Targets

水下目标识别是水声探测中的关键技术,具有重要的应用价值。海洋环境的复杂性导致水下目标识别中存在不可回避的噪声干扰。以人耳听觉机理为基础,提出了一种结合Gammatone滤波器、小波阈值降噪和希尔伯特-黄变换(HHT)的水下目标识别方法。采用Gammatone滤波器实现人耳听觉机理的模拟,并在此基础上进行小波阈值降噪,提高系统的噪声鲁棒性,然后利用HHT进行时频分析和特征提取。利用实际水下目标数据进行识别实验,对提出的方法进行了验证。实验结果表明,提出的方法在低信噪比条件下具有良好的鲁棒性,并具有较好的识别效果。

Underwater target recognition, as a key technique, places a great role in underwater acoustic detection. The inevitable noises decrease the performance of the system in the complex underwater acoustic environments. An underwater target recognition algorithm which includes Gammatone filter bank, wavelet threshold denoising and Hilbert-Huang transform （HHT） is proposed based on auditory percep- tion mechanism. Gammatone filter bank is used for the simulation of auditory perception, and the wavelet threshold denoising is applied to enhance the noise robustness of the system. Hilbert-Huang transform is employed as the time frequency analysis tool and used for feature extraction. At last, the efficiency of the proposed algorithm is testified by using the measured underwater target data in the recognition experiment. The results show that the proposed method has a robust performance and good accuracy under the condition of low SNR.