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Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks, Charles Strickland
Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks, Charles Strickland
Electronic Theses and Dissertations
This work proposes a neural network architecture that was designed to predict and reverse engineer frequency hopping jamming systems. The neural network was initially optimized for use with a 12th order linear shift feedback register maximum length sequence utilizing a minimal polynomial as the characteristic polynomial. This neural network was then scaled to accommodate 7 different sequences, of orders 6 through 12. The neural network was trained for these sequences using training data that is 10 times the length of the sequence. This information is then used to generate a hopping sequence that reduces the jamming interference to 0 with …