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Target Detection, Positioning and Tracking Using New UAV Gas Sensor Systems: Simulation and Analysis

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Abstract

The wilderness search and rescue (WiSAR) for lost persons or objects in remote wilderness areas is very challenging as these areas are almost inaccessible and the targets are concealed by the surrounding environment. The search and rescue will be very time-consuming and very costly. In this work, an unmanned aerial vehicle (UAV) equipped with gas sensor systems is designed and studied for target detection and positioning. A typical gas sensor is designed and integrated into an UAV to enable active detection of gas information (concentration and species at different locations) in the atmosphere and continuous target positioning. Novel UAV gas sensor systems have been modeled and simulated and simulation experiments with different gas sensor frequencies and sweeping modes have been carried out. A self-adaptive tracking team consisting of multiple UAV gas sensor systems has been simulated and analyzed. The results indicated that the new UAV gas sensor systems are very efficient and precise for the difficult search and rescue tasks in the wilderness environment.

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Acknowledgments

This work reported in this paper is the product of several research stages at George Mason University and Wuhan University of Technology has been sponsored in part by Natural Science Foundations of China (51579204 and 51679180), Double First-rate Project of WUT (472-20163042). Q. Li would like to acknowledge the support of Virginia Microelectronics Consortium (VMEC) research grant.

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Contributions

H.Y. and C.X. conceived and designed the experiments; H.Y., W.Z., Y.W and C.Z. performed the simulation and experiments; C.S., H.Y., K.J. and Z.Y. analyzed the data; H.Y., C.X., Y.W. and Q.L. wrote the paper. H.Y., C.X. and Q.L. conceived and design the study and experiments.

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Correspondence to Haiwen Yuan.

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Yuan, H., Xiao, C., Zhan, W. et al. Target Detection, Positioning and Tracking Using New UAV Gas Sensor Systems: Simulation and Analysis. J Intell Robot Syst 94, 871–882 (2019). https://doi.org/10.1007/s10846-018-0909-2

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