Two-Dimensional Jamming Recognition Algorithm Based on the Sevcik Fractal Dimension and Energy Concentration Property for UAV Frequency Hopping Systems

Abstract

Unmanned aircraft vehicle frequency hopping (UAV-FH) systems face multiple types of jamming, and one anti-jamming method cannot cope with all types of jamming. Therefore, the jamming signals of the environment where the UAV-FH system is located must be identified and classified; moreover, anti-jamming measures must be selected in accordance with different jamming types. First, the algorithm extracts the Sevcik fractal dimension from the frequency domain (SFDF) and the degree of energy concentration from the fractional Fourier domain of various types of jamming. Then, these parameters are combined into a two-dimensional feature vector and used as a feature parameter for classification and recognition. Lastly, a binary tree-based support vector machine (BT-SVM) multi-classifier is used to classify the jamming signal. Simulation results show that the feature parameters extracted by the proposed method have good separation and strong stability. Compared with the existing box-dimensional recognition algorithm, the new algorithm not only can quickly and accurately identify the type of jamming signal but also has more advantages when the jamming-to-noise ratio (JNR) is low.