基于小波与三次样条插值的包迹谱的水下目标分类研究

Study on classification of underwater targets based on modulation spectrum by wavelet transforms and cubic spline technique

研究了水下目标辐射噪声中包迹谱特征的提取。包迹波形由目标波形的极大值点连线构建而成。在对目标辐射噪声进行小波变换后,检索了波形极大值点,并采用三次样条插值算法,实现包迹波形的构建。根据构建波形提取包迹谱,最后把提取的特征送入模糊ART神经网络分类器进行识别。实验表明,这种方法对水下目标辐射噪声具有很好的分类效果,同时又能从其包迹谱中观察出原始波形不同程度的周期性。

One feature extraction method of modulation spectrum in the radiated noise of underwater targets was presented. Modulation curve is a linking route of maximum points of target waveshape. Maximum points of the target waveshape can be searched after applying the algorithm of wavelet transforms on the radiated noise. Then applying cubic spline technique, modulation curve are composed of these maximum points. Modulation spectrum is extracted from modulation curve. At last, the project is conducted by using fuzzy ART neural networks to the extracted character. The results show that it can efficiently classify underwater targets, at the same time it can show the period phenomenon of original wave.