Based On Chaos Theory And The Study Of The Two Key Technologies Of The Communication Countermeasure

As a new frontier, chaos theory has been widely used in various fields of naturaland social science for its many advantages. This thesis focuses on two specific problemsof chaotic signals applying in the communication confrontation fields, namely:1. The application of chaotic signals as a new kind of suppression interferencesource in the field of communication interference. This thesis first analyzes thestatistical properties of chaotic signals which make it possible as a new source ofsuppression interference instead of the traditional white Gaussian noise, then builds thebest digital communication system simulation model in the link level to provide aplatform for the comparing of the interference effects of the chaotic and Gaussian whitenoise. To verify the validity of the simulation model, four commonly digital modulationare used such as BPSK,QPSK,4ASK and16QAM. Then ingeniously derivates thetheoretical value of bit error rate of BPSK communication system in the Ulam chaoticnoise environment. Finally, several sets of simulation results not only verify thecorrectness of the theoretical value, but also come to the conclusion that in thecircumstance of low SNR, Ulam chaotic signals have better interference effects than thewhite Gaussian noise as a suppression interference source. These provide sometheoretical basis for the chaotic signals further using in the communication confrontationfields.2. The application of chaotic de-noising in the field of communicationreconnaissance. This thesis introduces the background and significance of the researchwork. Then some common used chaos de-noising algorithms are introduced with anemphasis on the two of them based on the phase space reconstruction, namely: theprincipal component analysis algorithm and the locally projective noise reductionalgorithm. Through the simulation results, not only the de-noising effects of the twoalgorithms are compared for the continuous and discrete chaotic signals, but also thefactors that affect the eventually de-noising effects of the two algorithms are analyzed.Then some improvements are made in this thesis and better de-noising effects are achieved.