Unmanned aerial vehicle (UAV) classification method and device based on dual frequency radar signal time-frequency distribution

A radar signal and time-frequency distribution technology, applied in the field of pattern recognition, can solve problems such as inability to accurately classify drones
CN107748862AInactive Publication Date: 2018-03-02TSINGHUA UNIV
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
TSINGHUA UNIV
Publication Date
2018-03-02
Estimated Expiration
Not applicable ยท inactive patent

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Abstract

The invention provides an unmanned aerial vehicle (UAV) classification method and device based on dual frequency radar signal time-frequency distribution. The method comprises steps that S1, short-time Fourier transform is utilized to process time domain data of each UAV acquired by a dual band radar system, and time-frequency maps of two bands of each UAV are acquired; S2, characteristic extraction of the time frequency maps of two bands of each UAV is carried out through a principal component analysis algorithm; and S3, for each UAV, the characteristics of the two bands of the UAV are fusedto acquire fusion characteristics, each fusion characteristic is taken as a sample to input to a support vector machine to classify the UAV. The method is advantaged in that the dual band radar systemis utilized to emit electromagnetic waves in different bands to the UAV, characteristic extraction of the micro Doppler information of UAV echo is carried out, then dual band characteristic fusion analysis is carried out, so different UAV categories are acquired, and UAV classification accuracy is improved.
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Description

technical field

[0001] The present invention relates to the field of pattern recognition, and more specifically, to a method and device for classifying drones based on the time-frequency distribution of dual-frequency radar signals. Background technique

[0002] A drone is an unmanned aircraft controlled by a radio device. With the increasing use of drones, the number has increased significantly, and it has also caused some security concerns. Therefore, it is of great importance to accurately detect and classify drones, determine their threat level, and provide effective basis for countermeasures. It is of great significance, and related requirements widely exist in anti-terrorism, civil aviation monitoring and other fields.

[0003] The existing UAV classification based on image recognition is easily affected by many factors, such as rainy weather, heavy fog, light, etc., the recognition accuracy is not high, and the recognition process is complicated and the amount of cal...

Claims

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