基于神经网络和小波分解的目标信号检测方法研究

将小波分解和神经网络相结合，应用于高海况、低信噪比条件下水中目标信号的特征提取中。文中首先对信号进行多尺度小波分解，利用目标信号功率主要集中在低频部分的特点，提取在不同频率带内信号的能量作为特征，然后利用人工神经网络对目标信号进行检测。在此基础上，通过不同浪级情况下海洋水压力场的仿真信号数据，对某型目标舰船的水压力信号进行了检测计算．验证了该方法的有效性，达到了在高海况、低信噪比条件下，目标信号检测率比较高、虚警率比较低的效果。

Research on Target Signal Detection Based on Neural Networks and Wavelet Decomposition

Characteristic extraction is crucial to detect the target signal. This paper combines a wavelet decomposition with neural networks for characteristic extraction under the condition of low SNR. The signal is first decomposed by wavelet transform, and the decomposed coefficients are reconstructed to form a new time series, from which some energy parameters can be extracted by time domain analysis. By using BP network, it is possible to recognize whether target signal is involved or not in received signals. The effectiveness of the method is verified by a real target signal with additive simulated noise signal, especially under the condition of low SNR.