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Chaotic Signal Processing With Application To Radar And Communication Countermeasures

Posted on:2005-06-21Degree:DoctorType:Dissertation
Country:ChinaCandidate:J C GanFull Text:PDF
GTID:1118360125963945Subject:Communication and Information System
Abstract/Summary:PDF Full Text Request
Electronic Warfare needs new idea and theory and technology due to development of modern Radar and communications. By appearance of chaos a fit spring breeze blows for science region, and chaos brings hopes for Electronic Warfare. Based on need of particularity of modern warfare and self-chaos development and perfectment, hopping frequency codes is regarded as main research object by exploiting chaos and other nonlinear theory to research new principle and technology of hopping frequency codes reconnaissance and jamming and weak signals detection and chaotic jamming in this paper. The main research production is as follows.At first of all, a weighted filtering algorithm is proposed. The algorithm can separate chaotic signals and Gaussian white noise by exploiting method of endowing with smaller weight for big eigenvalues and bigger weight for small eigenvalues of Jacobian matrix. The numerical simulation about noisy hopping frequency codes indicates that the method is effective.Secondly, the statistical characteristic and spectrum characteristic of chaotic signals are lucubrated. The investigation indicated that the Ulam chaotic mapping has spectrum same as Gaussian white noise. It illuminates that there is other distributing mode besides white noise Thirdly, the nonlinear prediction methods are lucubrated. (1) The speciality to let several coefficients of S-M algorithm are analyzed; (2) We first propose the nonlinear and adaptive prediction model based on several closest neighbors and extend the method to multistep prediction; (3) We first exploit Empirical Mode Decomposition (EMD) technology to predict hopping frequency codes, it is namely that a complex time series is decomposed as several Intrinsic Mode Functions (IMF) easy to prediction, then predict these IMF respectively, at last reconstruct estimated values of the time series; (4) We exploit the above prediction model validate effect while there are omitted data in observed time series and propose the corresponding resolve.Fourthly, the nonlinear filtering methods are lucubrated. (1) We develop the S-M filtering algorithm on the base of the S-M prediction algorithm; (2) We propose the nonlinear and adaptive filtering algorithm on the nonlinear and adaptive prediction model based on several closest neighbors; (3) We triumphantly detect the mistake data in the hopping frequency codes by exploiting the S-M filtering algorithm and the nonlinear and adaptive filtering model based on several closest neighbors.Fifthly, we lucubrate the chaotic characteristic of the real hopping frequency codes and propose the chaotic model of hopping frequency codes. (1) We research strange attractor of the free hopping frequency codes and noisy codes about hopping frequency communication radio and frequency agile radar, and calculate their correlative dimensions and largest Lyapunov exponents, and validate the predictability of the hopping frequency codes, at last find that these hopping frequency codes have chaotic characteristic; (2) We also research the chaotic characteristic of the new series composed of several chaotic time series, and find that the new series is also a chaotic series, but the values of the chaotic invariant are changed.Sixth, we lucubrate the nonlinear detection and identifying methods. (1) We proposed the time-changed dynamic models of radar signals based on dynamic theory, and can analyze quality and quantity of the deinterleaving pulse train; (2) We proposed the algorithm to computer selfsimilarity exponent and detect successfully the object echo buried in noise according to self-similarity of noise and nonsimilarity of sine wave; (3) We also proposed a nonlinear dynamic filtering detection model. Because the filtering model can capture primely dynamics of complex time series and distinguish object echo from clutter based on their dynamics, the method can be use to detect weak signals; (4) We also analyze the characteristic of Duffing oscillators with the changing of dynamics of the system, and research the technology to detect complex...
Keywords/Search Tags:Chaotic time series, Electronic Warfare, Prediction and filtering, Jamming, Signal processing
PDF Full Text Request
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