Sequence Generation And Analysis For Frequency Hopping Technology

In frequency hopping (FH) communications, if the carriers are captured, forecasted,and tracked successfully, the adverse part can easily interfere aiming at our next carrierfrequency. Thus our communication system may be paralysed. FH sequences can beused as the address code sequences to control the carrier FH. The randomness characterof the sequences is very important. It is the foundation and core factor to ensure the highanti-interference performance and the low probability of intercept in frequency-hoppingcommunications. And the quality of FH sequences will affect the performance ofcommunication systems directly.This paper structures analysis method of sequence FH character, studies thegeneration of the FH sequences, the FH character and the chaotic character of thechaotic sequences, and realizes software of FH sequence generation and characteristicsanalysis. The main research achievements are as follows:1. The paper structured characteristics analysis method of FH sequence, andrealized software of characteristics analysis. Especially, this paper improved cyclehamming correlation and presented S-Hamming correlation. In theory, cycle hammingcorrelation is used to Hamming characteristics. This method aims at FH sequence that isperiodic and smaller. But, if the cycle is too large or nearly not periodic, cycle hammingcorrelation has obvious shortage and is hard to achieve. S-Hamming correlation whichis compared to cycle hamming correlation is suitable to calculate cycle infinity orrelatively large cycle for application. This algorithm exploited the project applicationfield of the hamming correlation calculation.2. The paper mainly generated FH sequence bases on the m sequence, BluetoothFH sequence and chaotic FH sequence, analyzed FH characteristics. Aiming at thechaotic sequences generation, the paper proposed a quantitative algorithm based onhistogram equalization (HEBQ). The generation of chaotic FH sequence needsquantization and wide-interval. The traditional quantitative algorithm generallyconsidered the original chaotic system probability statistical characteristics. It’s difficultto search a quantity method if the probability density function is unknown or probabilitydensity function analytic expression is difficult to attain. Inspired by the histogramequalization theory in image processing, we present the quantitative algorithm based onhistogram equalization. Comparing with the prediction property and FH characteristics,the chaotic FH sequences generated using this algorithm have better balanceperformance and hamming correlation character, is hard to forecast and suitable for FHcommunications.3. The paper studies algorithms of chaos detection parameters and phase spacereconstruction parameters, which are applied in analysis of chaotic FH sequences, and then gets the chaotic characteristic parameters. Chaotic characteristics detection andphase reconstruction theories are used in analyzing chaotic FH sequence. We analyzechaotic characteristics of chaotic FH sequence. The fractal dimension and the maximumLyapunov exponent, which is critical to estimate the chaotic characteristics wereintroduced. Several algorithms analyzing time delay of phase space reconstruction andminimum embedding dimension was listed. This result can use to verify the selection ofprediction parameter.According to the structure of FH characteristics, generation of FH sequence,characteristics analysis and reconstruction parameter research, the researchachievements can be used to real FH communication and the FH sequence is difficult toprediction and detection.