Anti-jamming Method of Radio Fuze Based on KNN

In order to improve the anti-jamming ability of radio fuze and maximize the effectiveness of radio fuze in complex electronmagnetic environments, it is necessary to distinguish targets and jamming signals effectively. This research takes a sample of FM radio fuze as an example, mainly considers the sine amplitude modulation frequency-sweeping (Sine-AM) jamming signal and noise amplitude modulation frequency-sweeping (Noise-AM) jamming signal, which are pose the greatest threat to the FM radio fuze. The frequency exponential entropy and norm entropy of the fuze output signal are extracted. Then these features are input to K nearest neighbors classifier, in this way, the target signals and jamming signals can be effectively distinguished, Further improved the FM radio fuze ability to anti-jamming signal. The expermient result shows that using KNN classifier can get a classification accuracy more than 99%.