Multi-step prediction of frequency hopping sequences based on Bayesian inference

According to the chaotic characteristics of frequency hopping (FH) sequences and the short-term predictability of Chaos, this paper presents an improved Bayesian network predictive model applied to FH sequences prediction. Firstly, the model regards the entire reconstructed phase space as a prior data information; Then, according to the characteristic of FH sequences which consist of multiple frequency points, it constructs a local Bayesian network with the mutual information and an algorithm for Markov boundary; Finally, it achieves the multi-step prediction of FH by using the posterior inference algorithm. Theoretical results and large number of experiments show that the proposed Bayesian network predictive model has steady, real-time, effective and high-precision multi-step prediction ability, especially in small data set. Thus this model provides a novel method for the research and application of FH sequences prediction.