Hopping Code Sequence Modeling And Prediction

As the competition in frequency become more and more intensively in communication domain, the communication with fixed frequency suffers severe threats. In order to guarantee our communication, a new anti-Jamming communication system emerges, namely Frequency Hopping Communication System(FHCS). Duing to its outstanding counter-interference capability and Multiple Access properties, FHCS not only becomes popular in military communication, such as SINCGARS and JTIDS, but also is widely used in the civil mobile communications, including GSM, HomeRF and Bluetooth.FH communication has anti-scout and anti-jamming capability by escaping the jamming. It can work when the frequency hops irregularly and quickly, which can't be intercepted, recognised and interfered easily. It is interfered only when the signal frequency equals FH signal frequency. So the researches on anti-jamming and jamming of FHCS are a very important part of electronic warfare.The FH sequences which are used to control the frequency affects the anti-jamming capability crucially.In order to improve the anti-jamming and jamming capability of FHCS, the main work is to predict FH Sequence in the dissertation, including the following several aspects.1) The basic principle of FHCS's and FH sequences is presented, the components of the system are described detailedly, FH mutiple access and synchronization methods are summarized, the design for FH sequences is studied. At last, computer simulations are done.2) The construction principle of some familiar FH Sequences is analyzed. A novel predicting method aiming at the FH Sequences producted by shift-register is proposed. Computer simulations show the feasibility and efficiency of the method.3) Several nonlinear predicting methods are analyzed and studied. BP Neural Network, RBF Neural Network, Bernstein polynomial, Volterra adaptive filter are used for predicting. The advantages and disadvantages of each predicting method in predicting different kinds of sequences are analyzed. 4) Multi-step and multi-mode forecasting for chaotic time series are researched. Compare the performance of two multi-step forecasting methods according to theory analysis and experimental results. Multi-mode forecasting based on the above four nonlinear predicting methods is made, and the simulation results indicate that effective forecasting probability can be increased.