Extraction, Based On The Characteristics Of Higher Order Statistics And Wavelet Analysis

Recognition technique on underwater targets has important value not only in economic field, but also in military field. Most developed countries in the world pay much attention to it and a lot of achievements have been obtained. Although many scholars in our country have contributed to this field, some imperfections still exist in practice. The key of the recognition technique can hardly be obtained from abroad for some reasons. Following are the primary contributions:1) The history and development of recognition technique on underwater targets are introduced. Its academic methods and feature parameters that are employed before are summarized. Based on Higher-order Statistics (HOS) and wavelet theory, the features of some acoustic signals are extracted in this dissertation.2) The ship-radiated noise is studied by means of HOS that includes bi-spectrum and tri-spectrum on the basis of analyzing the properties of the noise. Then some features are extracted from Bi-spectrum and tri-spectrum.3) The features, which are respectively named wavelet multi-resolution energy feature and wavelet packet energy feature, are obtained by wavelet theory. And the simulation results show that the extracted features are effective.4) A generalized fractal dimension named multi-resolution energy fractal feature is extracted by combining wavelet theory and fractal theory. The results of recognition and classification show that the features are satisfactory.