Feature extraction method of radiated noise signal of underwater target

Abstract

The invention provides a feature extraction method of a radiated noise signal of an underwater target, comprising the steps of: firstly researching a visualization method of an acoustic signal to obtain a time-frequency visual image; then carrying out visual feature extraction method research including feature extraction and image centre torque feature extraction based on image texture by regarding a spectrogram in the time-frequency visual image as a searched object; on the basis, respectively utilizing principal component analysis (PCA) and linear discrimination analysis (LDA) algorithms to carry out optimal selection and dimension reduction on the visual feature obtained by the method, and finally applying the visual feature to simulation of underwater target identification. The experimental results show that the average recognition rates of three types of underwater noise signals under the mixed feature can be up to 91.97%, and the recognition rates of the selected visual feature parameters are obviously improved compared with the feature parameters extracted by the conventional method.

Description

technical field [0001] The invention relates to a feature extraction method of an underwater target radiation noise signal, Background technique [0002] Automatic identification of underwater targets is an important part of modern sonar and underwater acoustic countermeasure systems. The most critical technology in object recognition is feature extraction. Whether the feature parameters are effective depends on whether the category information contained in them is enough, and whether the interference information is small enough. If effective features cannot be used, even an excellent classifier cannot obtain ideal classification and recognition results. For decades, a series of feature extraction methods for underwater noise signals have emerged, mainly in the following categories: feature extraction based on spectral analysis, nonlinear feature extraction based on ship noise, feature extraction based on wavelet transform, A neural network feature extraction method and a ...

AI Technical Summary

Problems solved by technology

However, for complex environments, it is still a difficult problem to improve the robustness of the recognition system. In addition to solving problems such as channel mismatch, it is still necessary to continuously explore new robust feature extraction methods.

[0005] So far, there is no characteristic parameter that can be applied to underwater target recognition in different environments, so feature extraction for underwater noise signals is still an open research content

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