Title: Tensor feature extraction of underwater passive sonar target based on auditory model

Author(s): KANG Chunyu; XIA Zhijun; ZHANG Xinhua; ZHANG Yi; GUO Dexin;

Affiliation(s): Department of Underwater Weaponry and Chemical Defense, Dalian Navy Academy; Department of Graduate Student, Dalian Navy Academy

Abstract: Feature extraction is a key step for underwater passive sonar target classification and recognition. A kind of tensor feature extraction method based on auditory Patterson-Holdsworth cochlear model is proposed. First, the filter impulse response of the cochlear model is regarded as the basis function of signal decomposition, and the center frequency of different channels is determined according to the nonlinear scale or conventional linear scale of the auditory model. Then, the gain and bandwidth of the corresponding channel are calculated, and the order and phase parameters of the impulse response are quantified to obtain a relatively complete signal decomposition basis. And according to the principle of signal decomposition, the third-order tensor features of channel number-order number-phase number are obtained. Finally, the classification and recognition of the underwater passive sonar target is realized by calculating the similarity between the testing sample tensor feature and training sample tensor feature. The experiment on passive sonar target classification and recognition shows that the extracted tensor features have better classification and recognition performance, and the equivalent rectangular bandwidth scale of the auditory model is better than the linear scale to divide the center frequency, which can improve the target indication ability of passive sonar.