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Human Recognition Method Based on Multistatic Radar Micro-Doppler and Convolutional Neural Network

A convolutional neural network and human body recognition technology, applied in the field of radar target recognition, can solve problems such as difficulty in satisfying complex and diverse target recognition tasks, difficulty in making full use of correlation, etc., to alleviate echo signal differences and improve recognition accuracy , excellent performance

Active Publication Date: 2022-04-08
TSINGHUA UNIV
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Problems solved by technology

However, the classic target recognition method has many shortcomings mentioned above, and it is difficult to meet complex and diverse target recognition tasks; the simple majority vote fusion is difficult to make full use of the correlation between the data of each node, and the fusion effect still has room for improvement

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  • Human Recognition Method Based on Multistatic Radar Micro-Doppler and Convolutional Neural Network
  • Human Recognition Method Based on Multistatic Radar Micro-Doppler and Convolutional Neural Network
  • Human Recognition Method Based on Multistatic Radar Micro-Doppler and Convolutional Neural Network

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Embodiment Construction

[0037] The human body recognition method based on multistatic radar micro-Doppler and convolutional neural network proposed by the present invention, its flow chart is as follows figure 1 shown, including the following steps:

[0038] (1) Acquisition of known types of human body time domain echo signals:

[0039] A multistatic radar is used to collect time-domain echo signals of a known type of human body. The multistatic radar includes three receiving nodes with different positions, and the three receiving nodes simultaneously collect three-way time-domain echo signals s of the human body. 1 (t),s 2 (t),s 3 (t), where t is the acquisition time; in one embodiment of the present invention, the multistatic radar used is a chirp radar with a carrier frequency of 2.4GHz, a bandwidth of 45MHz, and a pulse repetition frequency of 5kHz, such as figure 2 As shown, it includes three nodes N1, N2, N3 arranged in a straight line at equal distances, among which N1, N2, N3 are all rec...

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Abstract

The invention relates to a human body recognition method based on multistatic radar micro-Doppler and convolutional neural network, and belongs to the technical field of radar target recognition. In this method, multi-base mines are used, which alleviates the difference of echo signals caused by the change of viewing angle, enhances the robustness of recognition, and improves the recognition accuracy. Convolutional neural network is used for data processing without manual design of features, which has certain versatility and excellent recognition accuracy. This method adopts transfer learning technology, uses RGB optical image pre-training weights in the convolutional neural network, and uses a three-channel multi-resolution time-frequency image with similar RGB optical images as the input of the convolutional neural network, after matching the pre-trained While training the weight dimension, it provides more information than the single-resolution time-frequency map, and this method can achieve good recognition accuracy in a variety of human recognition tasks.

Description

technical field [0001] The invention relates to a human body recognition method based on multistatic radar micro-Doppler and convolutional neural network, and belongs to the technical field of radar target recognition. Background technique [0002] Using radar to observe targets has the advantages of long-distance and all-weather all-weather. The radar micro-Doppler effect refers to the additional Doppler effect caused by the vibration and rotation of the target relative to the center of mass on the basis of the Doppler effect caused by the movement of the center of mass of the target. The micro-Doppler frequency shift of the target changes with time The law of change, that is, the micro-Doppler time-frequency map, can reflect the target's motion attitude and structural information, so it can be used as a feature of target recognition for application scenarios such as aircraft recognition, human posture recognition, and gesture recognition. Based on micro-Doppler features T...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S13/90G01S7/41
CPCG01S7/417G01S13/90G01S13/9017G01S13/9047
Inventor 李刚陈兆希
Owner TSINGHUA UNIV
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