Fingerprint Feature Recognition of Frequency Hopping Radio with FCBF-NMI Feature Selection

Abstract

High-dimensional features have great advantages for the analysis and identification of radio fingerprint features. In order to enhance the classification and recognition capability of frequency hopping stations, it is usually necessary to increase the feature type and dimension to further improve the classification accuracy of the classifier. However, with the increase of the feature types and the dimensions, a large number of irrelevant and redundant features will be introduced, which leads to the increased classification time and the low classification accuracy. In order to reduce the feature dimension and remove redundant features, a FCBF feature selection algorithm based on normalized mutual information was proposed, named FCBF-NMI. The algorithm uses normalized mutual information instead of symmetric uncertainty as the correlation evaluation standard of FCBF algorithm, and analyzes the correlation between features and categories, deletes irrelevant and redundant features, and finally obtains optimal feature subset. Experimental results show that, FCBF-NMI can obtain the reasonable optimal features, on the base of guaranteeing the correct classification rate, the computing time can be reduced, and the effectiveness of feature recognition and the generalization ability of classification algorithms can be improved as well.