A Time-Frequency Associated MUSIC Algorithm Research on Human Target Detection by Through-Wall Radar

Journal of Beijing Institute of Technology ›› 2022, Vol. 31 ›› Issue (1) : 123 -130.

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Journal of Beijing Institute of Technology ›› 2022, Vol. 31 ›› Issue (1) : 123 -130. DOI: 10.15918/j.jbit1004-0579.2021.087

A Time-Frequency Associated MUSIC Algorithm Research on Human Target Detection by Through-Wall Radar

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Abstract

In this paper, a time-frequency associated multiple signal classification (MUSIC) algorithm which is suitable for through-wall detection is proposed. The technology of detecting human targets by through-wall radar can be used to monitor the status and the location information of human targets behind the wall. However, the detection is out of order when classical MUSIC algorithm is applied to estimate the direction of arrival. In order to solve the problem, a time-frequency associated MUSIC algorithm suitable for through-wall detection and based on S-band stepped frequency continuous wave (SFCW) radar is researched. By associating inverse fast Fourier transform (IFFT) algorithm with MUSIC algorithm, the power enhancement of the target signal is completed according to the distance calculation results in the time domain. Then convert the signal to the frequency domain for direction of arrival (DOA) estimation. The simulations of two-dimensional human target detection in free space and the processing of measured data are completed. By comparing the processing results of the two algorithms on the measured data, accuracy of DOA estimation of proposed algorithm is more than 75%, which is 50% higher than classical MUSIC algorithm. It is verified that the distance and angle of human target can be effectively detected via proposed algorithm.

Keywords

through-wall radar / multiple signal classification (MUSIC) algorithm / inverse fast Fourier transform (IFFT) algorithm / target detection

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null. A Time-Frequency Associated MUSIC Algorithm Research on Human Target Detection by Through-Wall Radar. Journal of Beijing Institute of Technology, 2022, 31(1): 123-130 DOI:10.15918/j.jbit1004-0579.2021.087

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