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Remote Sens. 2017, 9(6), 594;

Classification of Personnel Targets with Baggage Using Dual-band Radar

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
The Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
Author to whom correspondence should be addressed.
Academic Editors: Francesco Soldovieri, Raffaele Persico and Prasad S. Thenkabail
Received: 29 April 2017 / Revised: 6 June 2017 / Accepted: 8 June 2017 / Published: 12 June 2017
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
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In this paper, we aim to identify passengers with different baggage by analyzing the micro-Doppler radar signatures corresponding to different kinds of gaits, which is helpful to improve the efficiency of security check in airports. After performing time-frequency analysis on the X-band and K-band radar data, three kinds of micro-Doppler features, i.e., the period, the Doppler offset, and the bandwidth, are extracted from the time-frequency domain. By combining the features extracted by dual-band radar with the one-versus-one support vector machine (SVM) classifier, three kinds of gaits, i.e., walking with no bag, walking with only one carry-on baggage by one hand, and walking with one carry-on baggage by one hand and one handbag by another hand, can be accurately classified. The experimental results based on the measured data demonstrate that the classification accuracy using dual-band radar is higher than that using only a single-band radar sensor. View Full-Text
Keywords: micro-Doppler; dual-band fusion; time-frequency analysis; feature extraction; gait classification micro-Doppler; dual-band fusion; time-frequency analysis; feature extraction; gait classification

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yang, L.; Chen, G.; Li, G. Classification of Personnel Targets with Baggage Using Dual-band Radar. Remote Sens. 2017, 9, 594.

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