Underwater Target Recognition Based on Improved Wavelet Energy Entropy
Abstract：A method based on improved wavelet energy entropy and probabilistic neural network for underwater target recognition was studied in the paper.Firstly,the radiated noise signal of underwater target is processed by using multi-resolution wavelet decomposition and reconstruction.Then,the sliding time window is introduced and the improved wavelet energy entropy of each decomposed band in sliding time window is extracted as feature vectors for target recognition.Finally,these feature vectors are used as input vectors of probabilistic neural network for target classification.The feature of signal under different frequency domain can be reflected by multi-resolution wavelet decomposition.While the feature of signal under different time domain can be reflected by the improved wavelet energy entropy which is defined by introducing the sliding time window.The improved wavelet energy entropy can reflect the time-frequency feature of signal at the same time and it is suitable to underwater target feature extraction.The result from test and simulation shows that the method is effective.
Keywords：target recognitionwavelet transformimproved wavelet energy entropyprobabilistic neural network
Publication Date：2012-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：3( 48-49,104 )