Pulse Repetition Interval Modulation Recognition Based on Frequencies and Patterns
Weidong Jin · Journal of Southwest Jiaotong University · 2007
According to the characteristics of pulse trains of radar signals, frequency and pattern are extracted from radar emitter signals. The two features constitute two-dimensional vectors, which are taken as inputs of a classifier designed by a support vector machine to identify the pulse repetition interval modulation of radar emitter signals automatically. Experimental results show that when the dimensions are lowered from 64 to 2, the extracted feature vector decreases the complexity of the classifier while maintaining or even enhancing the performances in recognition rate and noise suppression. Comparing to the original feature vector, the error rate of recognition of the extracted feature vector decreases from 0. 15% - 0. 25% to 0. 00% for the samples without noises, and from 0.40% - 1.30% to 0.15% - 0.93% for noised ones.