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.
Highlights
Opportunities for combat are fleeting in modern warfare, and each combat unit of war is connected by the data link as a whole; the real-time tactical intelligence can be shared among each combat unit.the reliability of information transmission is important to obtain a victorious war in the unmanned aircraft vehicle (UAV) data link; the anti-jamming performance of the UAV data link must be improved to ensure the reliability of data transmission [1]
The jamming faced by the UAV-frequency hopping (FH) system is mostly suppressed jamming, which includes broadband noise jamming (BNJ), narrowband noise jamming (NNJ), single-tone jamming (STJ), multi-tone jamming (MTJ), pulse jamming (PJ), and linear frequency modulation jamming (LFM) [2,3]
The present study proposes a jamming recognition method based on the Sevcik fractal dimension from the frequency domain (SFDF) and fractional Fourier domain energy concentration R f r of various jamming signals
Summary
Opportunities for combat are fleeting in modern warfare, and each combat unit of war is connected by the data link as a whole; the real-time tactical intelligence can be shared among each combat unit. Cheng et al adopted L-Z complexity and fractal box dimension as the characteristic parameters of interference recognition and used SVM to classify the jamming [19] This algorithm reduces computational complexity; the recognition performance is poor under low JNR. To solve this problem, the present study proposes a jamming recognition method based on the Sevcik fractal dimension from the frequency domain (SFDF) and fractional Fourier domain energy concentration R f r of various jamming signals. The present study proposes a jamming recognition method based on the Sevcik fractal dimension from the frequency domain (SFDF) and fractional Fourier domain energy concentration R f r of various jamming signals They constitute a two-dimensional feature parameter vector T = [D, Rfr ], which is used as a basis for jamming recognition.
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