Abstract
The rapid development of civil UAV promotes the social and economic development, and the frequent “flying illegally” events has brought great challenges to aviation safety and government supervision. The frequency hopping communication system used in UAV data transmission and control link has the advantages of anti-jamming and anti-interception, and its complex electromagnetic environment, which also brings great difficulties to UAV detection. In this paper, the detection of civil UAV is realized by frequency hopping signal monitoring. Firstly, by analyzing the signal characteristics of UAVs, an adaptive noise threshold calculation method is proposed for find the signals from spectrum data. Then, the improved clustering analysis algorithm is proposed based on constructed the waveform shape characteristics and peak characteristics of UAV frequency hopping signal. Finally, according to the designed experimental process, the experimental environment is set up, and the UAV monitoring, discovery and parameter estimation are realized by using the improved clustering analysis algorithm, and compared with K-means, K-means++, DBSCAN, Multi-hop and Auto-correlation methods. The results show that the method has certain robustness and has a good application prospect.
Highlights
Unmanned aerial vehicles (UAVs) of civil applications is growing rapidly across many application domains and improving ones’ quality of life, such as real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection, up till UAVs industry has greatly promoted social and economic development, and become thousands of billions market value [1]–[3]
Radio spectrum monitoring is through equipment to monitor the UAV frequency band all-weather, analyze the spectrum data, detect the control signal or the communication signal
An approach based on frequency difference and one-dimensional non-linear filter has been proposed in [18], which the robustness against white Gaussian noise has been enhanced but need SNR is greater than 13 dB which is an extremely harsh precondition, and is reflected in [20], the author propose a detection scheme based on the Neyman-Pearson test, and the results shows better than method of auto-correlation-based when the hopping period is short, and so on. the front papers methods almost set a harsh precondition, or manual parameter, and not make a real environment experiment within multiple frequency hopping signals active, lacking of persuasion of practical application
Summary
Unmanned aerial vehicles (UAVs) of civil applications is growing rapidly across many application domains and improving ones’ quality of life, such as real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection, up till UAVs industry has greatly promoted social and economic development, and become thousands of billions market value [1]–[3]. By focusing on frequency characteristics of the civil UAV control link signals (which are FH signals), a new FH signal detection of civil UAVs based on improved K-means clustering algorithm is proposed to fast discovery civil UAVs. Concretely, an adaptive noise threshold calculation method is firstly provided to preprocess spectrum data of civil UAVs in radio frequency band. From the data analysis point of view, the adaptive noise threshold calculation method is mainly used to extract the spectrum data higher than the noise threshold, the process is equal to obtain effective spectrum data of radio signals From these ideas, the FH signal of UAV is detected from the characteristics of signal duration continuity, signal frequency point connectivity, bandwidth similarity, waveform feature similarity, short-time peak energy similarity and time sequence correlation. Noise points and signal points in frames of spectrum data are convex distribution, which are shown in Figure 5, where θ is the signal threshold
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