Font Size: a A A

The Detection Of Weak Signal Embedded In Chaos

Posted on:2007-04-19Degree:MasterType:Thesis
Country:ChinaCandidate:Y ChenFull Text:PDF
GTID:2178360185978229Subject:Communication and Information System
Abstract/Summary:PDF Full Text Request
As we all known that people has been suffering noise interference for long time. This makes the research of the weak signal detection from noise to be the synthesis technique and top field in the scopes of measure. By thorough study of chaos, more and more researches show that a lot of weak signals are embedded in the strong chaotic noise. As a result, the extraction of weak signal from strong chaotic noise has been a research focus and also a difficult problem in these recent years.The main purpose of this thesis is to try to detect weak sine signal embedded in chaos. Three parts of research are included. First, calculating time delay and embedding dimension to reconstruct phase space. Second, based on chaos theories, the Artificial Neural Network is used to build one-step and multi-steps predictive model. Third, combining with an adaptive filter, predictive error is processed so that weak signal is extracted from strong chaotic noise.The main methods of this thesis are as following: chaotic time series is created by dynamics equation, then use GP algorithm to calculate embedding dimension and mutual information algorithm to calculate delay time. Based on Takens embedding theorem, the method utilizes the observed values of single variable of chaotic system to reconstruct phase space. Radial basis function neural network (RBFNN) is chosen to build predictive model. RBFNN is a special type of neural network linear-in-weight in nature and having nonlinear processing properties. Finally, an adaptive filter is applicable to do the followed weak signal extraction work.It can be concluded that the simulated chaotic time series models provide suitable tools for the weak sine signal detection from strong chaotic noise. This model can use to forest short-term chaotic time series. The experimented results show local predictability of...
Keywords/Search Tags:weak signal detection, chaotic time series, radial basis function neural network (RBFNN), phase space reconstruction, embedding dimension, delay time
PDF Full Text Request
Related items