| Summary: | The underwater acoustic monitoring acquires a wide range of frequency bands, including multi-band feature information in the recording process, and doped with various kinds of noise. In order to extract the effective feature information of each frequency band of the monitored signal reliably, avoid the influence of noise, solve the drawbacks of energy method in judging the characteristic frequency band, and break the limitation of single method in extracting multi-band feature information of broadband signal，in this paper, using the advantages of wavelet packet, correlation coefficient and Hilbert-Huang，multi-band feature information of broadband signals is extraded. Firstly, the fine frequency division of broadband signal is realized by wavelet packet algorithm, and the validity of node signal is judged and the noise nodes are eliminated by correlation coefficient analysis. Secondly, the effective components and noise in the effective node band are further separated by Hilbert-Huang. Finally, the effective band characteristics are obtained by reconstructing the node coefficients after processing.The results of simulation and experimental signal processing show that this method has certain advantages in extracting characteristic frequency information of broadband underwater acoustic monitoring signals, and can be popularized and applied in underwater acoustic target recognition and monitoring.
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