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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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
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Abstract	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.
AbstractList	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.
Author	Cao, Jiaxuan Chen, Yuxin Ding, Yipeng Peng, Yiqun Ouyang, Fangping
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SubjectTerms	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
Title	A Machine Learning-Based Algorithm for Through-Wall Target Tracking by Doppler TWR
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