Research On Intelligent Underwater Acoustic Signal Recognition Technology Based On Denoising Method

With the emergence of underwater acoustic imaging technology around the 1950 s,scholars from various countries have never stopped researching the underwater targets.The foreign countries have started to research earlier than our country in this regard.However,the early sonar images were all read manually,and the target recognition effect was poor.With the development of various denoising technologies,signal processing and artificial intelligence,more and more achievements have been made in the research of underwater environment.The unprocessed time-domain signals will almost never be the input in the research of underwater targets generally.The scholars always extract features from the signals for signal recognition.This thesis firstly studies the preprocessing methods of underwater targets based on the characteristics of the underwater environment,and mainly studies three different feature extraction methods.The first is short-time fourier transformation(STFT)which can be simply calculated and most widely used.The second is wavelet transformation(WT)with multi-resolution characteristics.The third is Cohen-like bilinear transformation.The Cohen-like bilinear transformation is also called the kernel function transformation,which includes many types.And the Wigner-Ville Distribution(WVD)is selected for research in this thesis.The three kinds of time-frequency transforms are applicable to different scenarios,and their ability to express signal characteristics is different for different types of signals.Secondly,this thesis studies the noise characteristics of actual underwater target signals and preprocesses the signals from the perspective of signal denoising.It mainly includes denoising methods based on empirical mode decomposition(EMD),singular value decomposition(SVD)and wavelet threshold.At the same time,an improved transform is proposed in the wavelet threshold denoising method.The three methods have different degrees of suppression of signal noise.Among them,the improved wavelet threshold denoising method has the best effect.It can greatly improve the signal-to-noise ratio(SNR)and reduce the mean square error of measured underwater acoustic signals in signal analysis.Finally,in the signal recognition stage,with the development of traditional machine learning,it is found that there have been many different types of improvements after investigation.Convolutional neural networks(CNN)are used to complete the classification from different types of time-frequency features of the measured underwater acoustic signals after denoising.Then we compare its performance with the results of the multi-step decision low frequency analysis recording(LOFAR)spectrum which is without denoising by using the accuracy and the confusion matrix.We find that the average recognition rate of the signal after denoising is 88.56%,while the recognition rate of the signal without denoising is 75.19%,which proves that denoising the signal is effective.At the same time,in view of the fact that there are fewer data samples for underwater environmental research,the method of generative adversarial networks(GAN)is used to increase the signal samples to improve the accuracy of deep learning,it is found that the final signal recognition rate reaches 96.673% after the experiments.Compared with the previous method based on the multi-step decision LOFAR spectrum which is without denoising preprocessing,the recognition rate of the signal can effectively be improve after the network added the data samples by generative adversarial networks.