【摘 要】
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The continuous change of communica-tion frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication net-works.Since the frequency-hopping (FH) sequence is usually generated by a certain model with certain regularity
【机 构】
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Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space,Ministry of Industry an
论文部分内容阅读
The continuous change of communica-tion frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication net-works.Since the frequency-hopping (FH) sequence is usually generated by a certain model with certain regularity,the FH frequency is thus predictable.In this paper,we investigate the FH frequency reconnais-sance and prediction of a non-cooperative communi-cation network by effective FH signal detection,time-frequency (TF) analysis,wavelet detection and fre-quency estimation.With the intercepted massive FH signal data,long short-term memory (LSTM) neural network model is constructed for FH frequency pre-diction.Simulation results show that our parameter es-timation methods could estimate frequency accurately in the presence of certain noise.Moreover,the LSTM-based scheme can effectively predict FH frequency and frequency interval.
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