Font Size: a A A

The Research Of Detection Of Underwater Acoustic Signals Based On The Chaotic Prediction Theory

Posted on:2008-11-12Degree:MasterType:Thesis
Country:ChinaCandidate:H C WangFull Text:PDF
GTID:2178360212478886Subject:Underwater Acoustics
Abstract/Summary:PDF Full Text Request
The detection of underwater acoustic signals has become so difficult with the complexities of the sea ambient as well as the improvement of the ship's noise reduction technology that it has obvious limited the application of traditional detection methods to underwater acoustic signals. Following the development of chaos theory in recent years, some people have studied the application of chaos theory to the field of underwater acoustic signal processing. For example, some people researched the mechanism of the underwater acoustic signals, prove the signals that have chaotic features, and some of them extracted the characteristic parameters. In this paper, we studied the chaotic feature extraction algorithm, noise reduction algorithm, detection and target recognition algorithms of underwater acoustic signals based on the chaotic theory. The main contributions of the paper are as follows:1. Focused on the research of two methods of choosing the neighborhood size parameter for the de-noise algorithm for underwater acoustic signals, a nonlinear local projective noise reduction algorithm is studied. The methods are based on recurrence plot and noise level estimation respectively. We also compared the noise reduction results of the Logistic, Lorenz and underwater acoustic signals with different noise level by the two parameter selection methods. The results show that the neighborhood size determined by noise level is superior to that determined by recurrence plot.2. The nonlinear characteristic parameters of the underwater acoustic signals were calculated. And after comparing the difference of nonlinear parameters between filtered and un-filtered underwater acoustic signals, the noise influence on the nonlinear characteristic parameters is concluded.3. Two chaotic time series prediction models were proposed. One based on RBF neural network and another on genetic algorithm (GA). After comparing the two models' learning rate, the number of training set and the forecast effect are concluded. The results show that the RBF neural network model is better than the GA model.4. After building up the two prediction models, a signal detection model based on chaotic forecast theory is explored. An examination criterion is proposed firstly for the detection model. We simulate the detection model by the Lorenz and underwater...
Keywords/Search Tags:Underwater acoustic signals, Chaos, State space reconstruction, Lyapunov exponent, Correlation dimension, h2 entropy, Local project noise reduction, prediction, RBF neural network, genetic algorithm, detection, support vector machine
PDF Full Text Request
Related items