Robust RFID Based 6-DoF Localization for Unmanned Aerial Vehicles

Zhang, Jian; Wang, Xiangyu; Yu, Zhitao; Lyu, Yibo; Mao, Shiwen; Periaswamy, Senthilkumar C. G.; Patton, Justin; Wang, Xuyu

doi:10.1109/access.2019.2922211

Cited by 51 publications

(24 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method is similar to the landmark-based method and requires a high density of tags. Furthermore, extending the problem to three dimensions, the indoor unmanned aerial vehicles localization and tracking problem was studied in [ 37 , 38 ].…”

Section: Related Workmentioning

confidence: 99%

Localization and Tracking of an Indoor Autonomous Vehicle Based on the Phase Difference of Passive UHF RFID Signals

Zhang

Gong

Liu

et al. 2021

Sensors

View full text Add to dashboard Cite

State-of-the-art radio frequency identification (RFID)-based indoor autonomous vehicles localization methods are mostly based on received signal strength indicator (RSSI) measurements. However, the accuracy of these methods is not high enough for real-world scenarios. To overcome this problem, a novel dual-frequency phase difference of arrival (PDOA) ranging-based indoor autonomous vehicle localization and tracking scheme was developed. Firstly, the method gets the distance between the RFID reader and the tag by dual-frequency PDOA ranging. Then, a maximum likelihood estimation and semi-definite programming (SDP)-based localization algorithm is utilized to calculate the position of the autonomous vehicles, which can mitigate the multipath ranging error and obtain a more accurate positioning result. Finally, vehicle traveling information and the position achieved by RFID localization are fused with a Kalman filter (KF). The proposed method can work in a low-density tag deployment environment. Simulation experiment results showed that the proposed vehicle localization and tracking method achieves centimeter-level mean tracking accuracy.

show abstract

Section: Related Workmentioning

confidence: 99%

Localization and Tracking of an Indoor Autonomous Vehicle Based on the Phase Difference of Passive UHF RFID Signals

Zhang

Gong

Liu

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In [8], Zhang et al [8] presented radio frequency identification for unmanned aerial vehicles that provides 6 degrees of freedom. ey attached modern ultrahigh frequency tags that are interrogated.…”

Section: Related Surveymentioning

confidence: 99%

RSSI-Controlled Long-Range Communication in Secured IoT-Enabled Unmanned Aerial Vehicles

Khan

Alturki

Alyamani

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

Unmanned aerial vehicle (UAV) has recently gained significant attention due to their efficient structures, cost-effectiveness, easy availability, and tendency to form an ad hoc wireless mobile network. IoT-enabled UAV is a new research domain that uses location tracking with the advancement of aerial technology. In this context, the importance of 3D aerial networks is attracting a lot of attention recently. It has various applications related to information processing, communication, and location-based services. Location identification of wireless nodes is a challenging job and of extreme importance. In this study, we introduced a novel technique for finding indoor and open-air three-dimensional (3D) areas of nodes by measuring the signal strength. The mathematical formulation is based on a path loss model and decision tree machine learning classifier. We constructed 2D and 3D models to gather more accurate information on the nodes. Simulation findings demonstrate that the proposed machine learning-based model excels in nodes location estimation, the actual and estimated distance of different nodes, and calculation of received signal strength in aerial ad hoc networks. In addition, the decision tree constructs an offline phase control in the flying vehicle’s location to enhance the time complexity along with experimental accuracy.

show abstract

“…A ground station processes the data from the cameras by a computer vision algorithm that generates a high-precision position estimate at high-frequency. Possibly, the cameras can be replaced by other ground localization systems without changing the overall paradigm [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. The ground station either can send the computed information to the drone, or, more usually, can pilot directly the UAV exploiting this data.…”

Section: Introductionmentioning

confidence: 99%

Development of Non Expensive Technologies for Precise Maneuvering of Completely Autonomous Unmanned Aerial Vehicles

Bigazzi

Gherardini

Innocenti

et al. 2021

Sensors

View full text Add to dashboard Cite

In this paper, solutions for precise maneuvering of an autonomous small (e.g., 350-class) Unmanned Aerial Vehicles (UAVs) are designed and implemented from smart modifications of non expensive mass market technologies. The considered class of vehicles suffers from light load, and, therefore, only a limited amount of sensors and computing devices can be installed on-board. Then, to make the prototype capable of moving autonomously along a fixed trajectory, a “cyber-pilot”, able on demand to replace the human operator, has been implemented on an embedded control board. This cyber-pilot overrides the commands thanks to a custom hardware signal mixer. The drone is able to localize itself in the environment without ground assistance by using a camera possibly mounted on a 3 Degrees Of Freedom (DOF) gimbal suspension. A computer vision system elaborates the video stream pointing out land markers with known absolute position and orientation. This information is fused with accelerations from a 6-DOF Inertial Measurement Unit (IMU) to generate a “virtual sensor” which provides refined estimates of the pose, the absolute position, the speed and the angular velocities of the drone. Due to the importance of this sensor, several fusion strategies have been investigated. The resulting data are, finally, fed to a control algorithm featuring a number of uncoupled digital PID controllers which work to bring to zero the displacement from the desired trajectory.

show abstract

Robust RFID Based 6-DoF Localization for Unmanned Aerial Vehicles

Cited by 51 publications

References 35 publications

Localization and Tracking of an Indoor Autonomous Vehicle Based on the Phase Difference of Passive UHF RFID Signals

Localization and Tracking of an Indoor Autonomous Vehicle Based on the Phase Difference of Passive UHF RFID Signals

RSSI-Controlled Long-Range Communication in Secured IoT-Enabled Unmanned Aerial Vehicles

Development of Non Expensive Technologies for Precise Maneuvering of Completely Autonomous Unmanned Aerial Vehicles

Contact Info

Product

Resources

About