Underwater Target Recognition Based on Wavelet Packet and Principal Component Analysis

Underwater target classification and recognition is a research challenge of signal processing application,and as a result of complicated target signal and diverse ingredient,the character data are great and with high dimension,which needs huge calculating cost.Under this situation,a new approach to extracting noise radiated from underwater target based on wavelet packet and principal component analysis is presented.Firstly Initial characteristics are obtained from underwater target by using decomposition and reconstruction of wavelet packet.Then principal component analysis is used to get the final characteristics.The final characteristics are used by designed neural network to recognize the noise radiated from underwater target.Experiment results show that the method of extracting features has better classification effect with low calculating cost.