Unmanned aerial vehicle flight control signal visual recognition sorting method

Abstract

The invention discloses an unmanned aerial vehicle flight control signal visual recognition sorting method. The method comprises the steps of obtaining an unmanned aerial vehicle flight control signalto be analyzed; performing time-frequency analysis and image denoising on the signals and extracting signal parameters; and performing clustering analysis and time-frequency diagram reconstruction onthe signal parameters to obtain a final unmanned aerial vehicle flight control signal visual recognition sorting result. Technical design is carried out for solving the bottleneck problem of unmannedaerial vehicle investigation. Various algorithm technologies such as signal time-frequency analysis, image genetic algorithm segmentation and denoising, image connected region marking feature extraction, density peak clustering analysis based on kernel density estimation and time-frequency image reconstruction are used; the method realizes detection and recognition of the flight control signal ofthe unmanned aerial vehicle, is very convenient to implement, can effectively avoid the defects of other reconnaissance means, can effectively help a commander to analyze, identify and sort the radiofrequency hopping signal timely, accurately and visually, and provides powerful support for the operator to study and judge the signal property.

Description

technical field [0001] The invention belongs to the field of digital signal processing, and in particular relates to a method for visual recognition and sorting of unmanned aerial vehicle flight control signals. Background technique [0002] With the development of economy and technology and the improvement of people's living standards, drones have been widely used in people's production and life, bringing endless convenience to people's production and life. With the rapid opening of the UAV market, the UAV industry has developed rapidly. However, in recent years, accidents of "indiscriminate flying" and "black flying" of drones have occurred frequently, injuring people and destroying property, and may even be used by terrorists, causing major safety hazards. Therefore, only by establishing and improving the corresponding prevention and control system, timely detection and discovery of suspicious targets, and appropriate means to drive away and prevent and control can effec...

AI Technical Summary

Problems solved by technology

[0009] The frequency hopping signal sorting method based on the KHM clustering algorithm based on K harmonic means uses the information of the frequency hopping signal that has been obtained for sorting. The sorting result is not sensitive to the center of the initialization, and the effect is good, but it cannot solve the problem that the sample point is in the center of the initialization. To some extent, there is a problem of "it can belong to both class A and class B", which leads to unsatisfactory sorting results

[0010] Therefore, although there are many existing technologies, some of these methods have poor anti-interference performance, some require the support of prior knowledge, and some need to set various initial parameters in advance, and are sensitive to initial parameters and are more restricted.

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