Prediction of confidence interval of target frequency hopping sequence based on network station sorting
The constant changes in communication frequencies bring difficulties to the reconnaissance and pre-diction of non-cooperative communication networks.Since frequency hopping sequences are usually generated by specific models with certain regularity,frequency hopping sequences are predictable.The target frequency hop-ping sequence confidence interval prediction based on network station selection is studied by extracting frequency hopping signal parameters and a network station selection algorithm based on time and frequency two-dimensional information.Using a large amount of intercepted frequency hopping signal data,a long short-term memory neural network model is constructed for frequency hopping sequence prediction.The simulation results show that even under the influence of certain noise,the sorting method can still accurately sort the target frequency hopping sig-nals.In addition,the LSTM-based prediction method can effectively predict frequency hopping sequence confi-dence levels and confidence intervals.
network station sortingfrequency hopping sequence predictionconfidence levelconfidence in-tervalLSTM
NETL
NSTL
万方数据
