Classification of Personnel Targets with Baggage Using Dual-band Radar
AbstractIn 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
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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.
Yang L, Chen G, Li G. Classification of Personnel Targets with Baggage Using Dual-band Radar. Remote Sensing. 2017; 9(6):594.Chicago/Turabian Style
Yang, Le; Chen, Gao; Li, Gang. 2017. "Classification of Personnel Targets with Baggage Using Dual-band Radar." Remote Sens. 9, no. 6: 594.
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