基于CMFS-MIC特征选择的跳频电台个体识别方法

ue to the presence of irrelevant features and redundant features in the fine feature set of frequency hopping radio stations, the large amount of calculation and low recognition accuracy are existed for radio stations identification. This paper proposed an individual identification method for frequency hopping radio stations based on CMFS-MIC feature selection. Firstly, the fine feature sets of each collected signal sample of frequency hopping radio were calculated, and then using the association information entropy to measure the combination effect of the feature subsets, taking into account the features relationship and the redundant relationship to sort each feature in descending order. On this basis, the approximate Markov blanket method using the largest information coefficient metric is used to remove redundant and irrelevant features. This process achieves the purpose of optimizing and reducing the dimension of feature subsets. Finally, it designed a voting combination classifier to realize the identification of four frequency-hopping radio signals. Simulation results show that proposed algorithm has higher sorting recognition rate.