Unmanned aerial vehicle classification method and device based on radar multipath signal time-frequency feature fusion

A time-frequency feature and multi-path signal technology, applied in the field of radar, can solve the problems of large error in target tracking and detection, great influence of climate, temperature and light conditions, low recognition accuracy, etc., and achieve the effect of accurate distinction

Active Publication Date: 2019-03-01
TSINGHUA UNIV
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Problems solved by technology

[0008] In related technologies, images obtained based on optical imaging or monitoring data transmitted by sensors have high requirements on the surrounding environment, are greatly affected by climate, temperature and light conditions, and also have the problem

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  • Unmanned aerial vehicle classification method and device based on radar multipath signal time-frequency feature fusion
  • Unmanned aerial vehicle classification method and device based on radar multipath signal time-frequency feature fusion
  • Unmanned aerial vehicle classification method and device based on radar multipath signal time-frequency feature fusion

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Embodiment Construction

[0043] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0044] The following describes the UAV classification method and device based on the time-frequency feature fusion of radar multipath signals according to the embodiments of the present invention with reference to the accompanying drawings. Feature Fusion for UAV Classification.

[0045] figure 1 It is a flow chart of the UAV classification method based on the time-frequency feature fusion of radar multipath signals according to the embodiment of the present invention.

[0046] Such as figure 1 As shown, the UAV classification...

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Abstract

The invention discloses an unmanned aerial vehicle classification method and device based on radar multipath signal time-frequency characteristic fusion. The method includes collecting radar data of multiple UAVs; radar data are processed by Fourier transform to obtain radar echoes of different range units and different time, and static clutter of echo signals is suppressed by high-pass filter toextract different multipath echo signals of different range units. The multi-path echo signal is analyzed by short-time Fourier transform to obtain the time-frequency map of each UAV's multi-path echosignal. Extracting Time-Frequency Features of Multipath Echo Signal from Time-Frequency Diagram; and fusing the time-frequency characteristics of the multipath echo signals to obtain the classification results of each unmanned aerial vehicle. This method can acquire the data of different targets for learning and training, and does not need to modify the hardware, so as to accurately distinguish between bird and UAV, different types of UAV function, and high accuracy is achieved.

Description

technical field [0001] The invention relates to the field of radar technology, in particular to a method and device for classifying unmanned aerial vehicles based on time-frequency feature fusion of radar multipath signals. Background technique [0002] A drone is an unmanned aircraft that uses radio equipment for control and manipulation. In recent years, with the mature development of materials, communication and other technologies, the cost of spare parts such as sensors and cameras has been reduced, making the drone market maintain rapid growth. In addition, the role of drones in agriculture and forestry, surveying and mapping, power inspection, disaster relief, and security has become increasingly apparent. However, with the increasing use of drones, the number has increased significantly, and it has also caused some safety concerns. For example, the frequent incidents of "black flight disturbance" of drones have repeatedly threatened the safety of traffic management i...

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Application Information

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IPC IPC(8): G06K9/00
CPCG06F2218/02G06F2218/08G06F2218/12
Inventor 李刚章鹏飞
Owner TSINGHUA UNIV
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