A Machine Learning-Based Algorithm for Through-Wall Target Tracking by Doppler TWR

Doppler through-wall radar (TWR) enables noncontact behind-the-wall target trajectory tracking, which has a wide range of application scenarios in the field of detection. However, when facing unknown wall parameters, the detection accuracy of Doppler TWR becomes severely limited. Hence, in this work...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 9
Main Authors: Cao, Jiaxuan, Ding, Yipeng, Peng, Yiqun, Chen, Yuxin, Ouyang, Fangping
Format: Journal Article
Language:English
Published: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Back propagation networks
Backpropagation neural network (BPNN)
Doppler effect
Doppler radar
Doppler through-wall radar (TWR)
Electromagnetic scattering
Fourier transforms
Machine learning
Neural networks
Radar tracking
Receivers
support vector machine (SVM)
Support vector machines
Target tracking
Thickness
Tracking
Trajectory
trajectory correction
wall thickness
ISSN:0018-9456, 1557-9662
Online Access:Get full text
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Summary:Doppler through-wall radar (TWR) enables noncontact behind-the-wall target trajectory tracking, which has a wide range of application scenarios in the field of detection. However, when facing unknown wall parameters, the detection accuracy of Doppler TWR becomes severely limited. Hence, in this work, we propose a machine learning-based target tracking algorithm for through-wall sensing applications. First, using the peak search method based on the short-time Fourier transform (STFT) to obtain a roughly predicted trajectory under the free-space assumption. Then, a classifier based on support vector machine (SVM) is used to estimate the wall thickness from the predicted target trajectory. Finally, a backpropagation neural network (BPNN) is constructed to obtain the corrected target trajectory, whose inputs are the estimated wall thickness and the predicted target trajectory. Experimental results demonstrate that the proposed algorithm significantly improves target tracking accuracy in through-wall detection applications, achieving up to an 80% improvement compared to traditional methods.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2024.3369133