Human behavior micro-Doppler classification and recognition method based on migration deep neural network

A deep neural network, classification and recognition technology, applied in the field of radar target micro-Doppler effect analysis, can solve problems such as difficulty in extracting limbs, achieve the effect of improving performance and reducing requirements

Inactive Publication Date: 2022-01-04
NAT UNIV OF DEFENSE TECH
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AI Technical Summary

Problems solved by technology

Due to the particularity of the composition of human targets, the instantaneous feature intensity of the whole human body (trunk and head) in radar echoes is much greater than that of limbs, so it is often difficult to directly extract the instantaneous features of limbs in radar echoes

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  • Human behavior micro-Doppler classification and recognition method based on migration deep neural network
  • Human behavior micro-Doppler classification and recognition method based on migration deep neural network
  • Human behavior micro-Doppler classification and recognition method based on migration deep neural network

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Embodiment Construction

[0034] The present invention is further explained below.

[0035] The first step is to perform clutter suppression on the radar echo

[0036] The background cancellation method based on the adaptive Least Mean Square (LMS) algorithm is used to design the filter, and the clutter near zero frequency is suppressed by using the difference between the moving target and the background clutter at the Doppler frequency .

[0037] The basic idea of ​​the adaptive background cancellation method is to adjust the weighting coefficients in real time according to the current echo data, so as to obtain the latest estimate of the background. Fig. 2(a) shows the schematic diagram of adaptive background cancellation, and Fig. 2(b) is the calculation flow chart of adaptive background cancellation algorithm. It can be seen from the figure that the current background in the adaptive background cancellation process is the weighted sum of the input sequence, namely

[0038] b(m,n)=w T (m)D(m,n) ...

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Abstract

The invention discloses a human behavior micro-Doppler recognition method based on a deep migration network, which utilizes ImageNet and other large-scale open source natural image databases to pre-train the deep neural network, optimize the network weights of the deep network, and then perform different behavioral Micro-Doppler spectrum is used for supervised learning. During the learning process, the network weights of the convolutional network are frozen, and only the fully connected layer of the deep network is trained. The training cost function is the combination of the Softmax function and the natural image and micro-Doppler spectrum. The sum of entropy differences, the trained neural network can be used to effectively distinguish the micro-Doppler time spectrum of different behaviors, so as to realize the classification and recognition of human behaviors. The invention adopts migration learning to optimize the network weight of the deep convolutional network, which can effectively reduce the requirement of the deep neural network for the training data set and improve the performance of classification and recognition.

Description

technical field [0001] The invention relates to the field of radar target micro-Doppler effect analysis, in particular to a human behavior micro-Doppler classification and recognition method based on migration deep neural network. Background technique [0002] Radar can locate and identify targets by emitting and receiving electromagnetic waves. For a moving target, the frequency of the transmitted and received echoes will change with the moving speed of the target, which is the Doppler effect. Since the movement of the human body is a non-rigid body movement, each part of the body has its own regular movement during the movement process. This frequency modulation of each limb added to the human body is called the micro-Doppler effect. The micro-Doppler effect is not sensitive to distance, light conditions and background complexity, and micro-Doppler features corresponding to different behaviors can be extracted and analyzed for analysis and estimation of human motion chara...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S7/41G06N3/08
CPCG01S7/417G06N3/08
Inventor 金添杜浩宋勇平戴永鹏
Owner NAT UNIV OF DEFENSE TECH
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