水下目标工频磁场扰动信号检测方法研究

为了解决水下目标磁场近程化探测中磁信号衰减快、干扰强及扰动特征不明确、无法有效探测信号等问题,提出了一种基于混合神经网络与注意力机制(Att-CNN-GRU)的工频磁场水下目标时间序列扰动信号检测方法。将CNN,GRU神经网络与Attention机制相结合拟合信号,构建分类神经网络,对目标信号进行分类识别,同时与未引入注意力机制的CNN-LSTM模型及单一CNN和LSTM网络模型的预测及检测性能进行比较。结果表明,相较于传统方法,信号拟合效果将误差分别减小了36.24%,14.44%和4.878%,目标检测准确率达到83.3%。因此,加入Attention机制的CNN-GRU模型检测性能比CNN,LSTM和CNN-GRU模型更优异,作为辅助手段,能有效解决工频磁场探测中扰动信号微弱、扰动规律不明确、背景噪声多等问题,实现对水下目标造成的工频磁场扰动信号的拟合与检测。

Study on detection method of power frequency magnetic field disturbance signal for underwater target

A hybrid neural network and attention mechanism (Att-CNN-GRU) is presented to solve the problems of fast attenuation,strong interference,ambiguous disturbance characteristics and ineffective signal detection in magnetic field proximity detection of underwater targets.A method for detecting time series disturbance signal of underwater target with power frequency magnetic field is presented.The method combines CNN,GRU neural network and Attention mechanism to fit the signal,and constructs a classification neural network to classify and identify the target signal.The method is compared with the prediction and detection performance of CNN-LSTM model without attention mechanism and single CNN and LSTM network model.The results show that the error of signal fitting is reduced by BF]36.24%BFQ],BF]14.44%BFQ],BF]4.878%BFQ] and the target detection accuracy is BF]83.3%BFQ] compared with the traditional methods.Therefore,the CNN-GRU model with Attention mechanism has better performance than CNN,LSTM and CNN-GRU models.As an auxiliary means,it can effectively solve the problems of weak disturbance signal,unclear disturbance law and more background noise in power frequency magnetic field detection,to realize the fitting and detection of power frequency magnetic disturbance signal to underwater target.