In resent years, providing location services for mobile targets in a closed environment has been a growing interest. In order to provide good localization and tracking performance for drones in GPS-denied scenarios, this paper proposes a multi-tag radio frequency identification (RFID) system that is easy to equip and does not take up the limited resources of the drone which is not susceptible to processor performance and cost constraints compared with computer vision based approaches. The passive RFID tags, no battery equipped, have an ultra-high resolution of millimeter level. We attach multiple tags to the drone and form multiple sets of virtual antenna arrays during motion, avoiding arranging redundant antennas in applications, and calibrating the speed chain to improve tracking performance. After combining the strap-down inertial navigation system (SINS) carried by the drone, we have established a coupled integration model that can suppress the drift error of SINS with time. The experiment was designed in bi-dimensional and three-dimensional scenarios, and the integrated positioning system based on SINS/RFID was evaluated. Finally, we discussed the impact of some parameters, this innovative approach is verified in real scenarios.
Xiang LU
University of Science and Technology of China
Ziyang CHEN
University of Science and Technology of China
Lianpo WANG
University of Science and Technology of China
Ruidong LI
National Institute of Information and Communications and Technology (NICT)
Chao ZHAI
University of Science and Technology of China
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Xiang LU, Ziyang CHEN, Lianpo WANG, Ruidong LI, Chao ZHAI, "RF-Drone: Multi-Tag System for RF-ID Enables Drone Tracking in GPS-Denied Environments" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 10, pp. 1941-1950, October 2019, doi: 10.1587/transcom.2018DRP0005.
Abstract: In resent years, providing location services for mobile targets in a closed environment has been a growing interest. In order to provide good localization and tracking performance for drones in GPS-denied scenarios, this paper proposes a multi-tag radio frequency identification (RFID) system that is easy to equip and does not take up the limited resources of the drone which is not susceptible to processor performance and cost constraints compared with computer vision based approaches. The passive RFID tags, no battery equipped, have an ultra-high resolution of millimeter level. We attach multiple tags to the drone and form multiple sets of virtual antenna arrays during motion, avoiding arranging redundant antennas in applications, and calibrating the speed chain to improve tracking performance. After combining the strap-down inertial navigation system (SINS) carried by the drone, we have established a coupled integration model that can suppress the drift error of SINS with time. The experiment was designed in bi-dimensional and three-dimensional scenarios, and the integrated positioning system based on SINS/RFID was evaluated. Finally, we discussed the impact of some parameters, this innovative approach is verified in real scenarios.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018DRP0005/_p
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@ARTICLE{e102-b_10_1941,
author={Xiang LU, Ziyang CHEN, Lianpo WANG, Ruidong LI, Chao ZHAI, },
journal={IEICE TRANSACTIONS on Communications},
title={RF-Drone: Multi-Tag System for RF-ID Enables Drone Tracking in GPS-Denied Environments},
year={2019},
volume={E102-B},
number={10},
pages={1941-1950},
abstract={In resent years, providing location services for mobile targets in a closed environment has been a growing interest. In order to provide good localization and tracking performance for drones in GPS-denied scenarios, this paper proposes a multi-tag radio frequency identification (RFID) system that is easy to equip and does not take up the limited resources of the drone which is not susceptible to processor performance and cost constraints compared with computer vision based approaches. The passive RFID tags, no battery equipped, have an ultra-high resolution of millimeter level. We attach multiple tags to the drone and form multiple sets of virtual antenna arrays during motion, avoiding arranging redundant antennas in applications, and calibrating the speed chain to improve tracking performance. After combining the strap-down inertial navigation system (SINS) carried by the drone, we have established a coupled integration model that can suppress the drift error of SINS with time. The experiment was designed in bi-dimensional and three-dimensional scenarios, and the integrated positioning system based on SINS/RFID was evaluated. Finally, we discussed the impact of some parameters, this innovative approach is verified in real scenarios.},
keywords={},
doi={10.1587/transcom.2018DRP0005},
ISSN={1745-1345},
month={October},}
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TY - JOUR
TI - RF-Drone: Multi-Tag System for RF-ID Enables Drone Tracking in GPS-Denied Environments
T2 - IEICE TRANSACTIONS on Communications
SP - 1941
EP - 1950
AU - Xiang LU
AU - Ziyang CHEN
AU - Lianpo WANG
AU - Ruidong LI
AU - Chao ZHAI
PY - 2019
DO - 10.1587/transcom.2018DRP0005
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2019
AB - In resent years, providing location services for mobile targets in a closed environment has been a growing interest. In order to provide good localization and tracking performance for drones in GPS-denied scenarios, this paper proposes a multi-tag radio frequency identification (RFID) system that is easy to equip and does not take up the limited resources of the drone which is not susceptible to processor performance and cost constraints compared with computer vision based approaches. The passive RFID tags, no battery equipped, have an ultra-high resolution of millimeter level. We attach multiple tags to the drone and form multiple sets of virtual antenna arrays during motion, avoiding arranging redundant antennas in applications, and calibrating the speed chain to improve tracking performance. After combining the strap-down inertial navigation system (SINS) carried by the drone, we have established a coupled integration model that can suppress the drift error of SINS with time. The experiment was designed in bi-dimensional and three-dimensional scenarios, and the integrated positioning system based on SINS/RFID was evaluated. Finally, we discussed the impact of some parameters, this innovative approach is verified in real scenarios.
ER -