Channel matching of shallow water active detection combined with convolutional neural network

Channel matching is an important method to improve the detection SNR in the field of active detection.Aiming at the problem of active detection channel matching in the environment of unascertained seabed parameters,a channel matching algorithm combined with convolution neural network is proposed.The algorithm is based on the seabed parameter disturbance to carry out sound field simulation and generate convolution network training data.Firstly,the signals are classified according to the seabed sediment types by classification network,and the seabed parameters are retrieved by using a separate convolution network in each classification interval.Then,the acoustic field model is used to estimate the channel transfer function for channel matching,so as to suppress the multipath effect and improve the echo detection ability in the unascertained environment.Simulation and experimental results show that the algorithm can effectively estimate the channel transfer function and realize the optimal channel matching under the condition of uncertain seabed environment.Under the experimental conditions,the SNR of echo detection can be improved by about 4 dB.Compared with the traditional methods,this algorithm can achieve channel matching for low SNR signals under the condition of uncertain seabed parameters,and does not need high SNR experimental reference signals,which effectively improves the environmental tolerance of the channel matching method.