Study On Modulation Identification Algorithms And Its Application In Monitoring

Radio signal monotoring refers to detect, search and receive modulated radio signals, then analysis, identify and monitor the feature parameters of signals. To the process of signal modulation, it means that judging and estimating the characteristic parameters of modulated signals without any priori informations. The first step of monotoring radio signals is analysising and estimating the features of modulated signals, therefore modulation recognition of siganals plays an important role in the field of radio signal monitoring.This paper studies the modulation recognition with a genaral strategy of combining advanced algorithms. First we introduce12kinds of common communication signals, and gives the features of instant amplititude, instant phase and instant frequency and so on. We also disscuss the impact of noises to the modulated signals. After analysising and comparing different signals modulation recognition algorithms, first we focus on studying the wavelet transform on denoising and the selection of signal’s abrupt boundary, and then analysis the advantages of the maximum spectral density feature method and the Kernel fisher discriminant algorithm. With a synthesize strategy of combining these algorithms, we can solves the distinguishability to all the signals step by step. In the simulation experiments parts, the results illustrates that the multi-class signals identification algortihm can achieves above90%precision under the case of low SNR by choosing appropriate wavelet function and threshhold value. It also proves that this classifier strategy has a high and steady success rate, especially to the case of low SNR(0dB～5dB) in the computer simulation experiments.