Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/rs9060594 | REMOTE SENSING |
Keywords | Field | DocType |
micro-Doppler,dual-band fusion,time-frequency analysis,feature extraction,gait classification | Radar engineering details,Radar,Computer vision,Support vector machine,Feature extraction,Bandwidth (signal processing),Artificial intelligence,Time–frequency analysis,Geology,Classifier (linguistics),Offset (computer science) | Journal |
Volume | Issue | ISSN |
9 | 6 | 2072-4292 |
Citations | PageRank | References |
1 | 0.36 | 12 |
Authors | ||
3 | ||