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UAV Localization with Unreliable Observations in Hostile Underground Environments

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  • Computer Networks and Distributed Computing
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Abstract

The accurate and robust unmanned aerial vehicle (UAV) localization is significant due to the requirements of safety-critical monitoring and emergency wireless communication in hostile underground environments. Existing range-based localization approaches fundamentally rely on the assumption that the environment is relatively ideal, which enables a precise range for localization. However, radio propagation in the underground environments may be dramatically influenced by various equipments, obstacles, and ambient noises. In this case, inaccurate range measurements and intermittent ranging failures inevitably occur, which leads to severe localization performance degradation. To address the challenges, a novel UAV localization scheme is proposed in this paper, which can effectively handle unreliable observations in hostile underground environments. We first propose an adaptive extended Kalman filter (EKF) based on the fusion of ultra-wideband (UWB) and inertial measurement unit (IMU) to detect and adjust the inaccurate range measurements. Aiming to deal with intermittent ranging failures, we further design the constraint condition by limiting the system state. Specifically, the auto-regressive model is proposed to implement the localization in the ranging blind areas by reconstructing the lost measurements. Finally, extensive simulations have been conducted to verify the effectiveness. We carry out field experiments in an underground garage and a coal mine based on P440 UWB sensors. Results show that the localization accuracy is improved by 16.9% compared with the recent methods in the hostile underground environments.

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Peng-Peng Chen  (陈朋朋), Kui-Yuan Zhang  (张奎元), Shou-Wan Gao  (高守婉) & Si-Yi Ren  (任思怡)

  2. Mine Digitization Engineering Research Center, Ministry of Education, Xuzhou, 221116, China

    Peng-Peng Chen  (陈朋朋) & Shou-Wan Gao  (高守婉)

  3. Shenzhen Research Institute, China University of Mining and Technology, Shenzhen, 518057, China

    Peng-Peng Chen  (陈朋朋)

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  1. Peng-Peng Chen  (陈朋朋)
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  2. Kui-Yuan Zhang  (张奎元)
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  3. Shou-Wan Gao  (高守婉)
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Corresponding author

Correspondence to Shou-Wan Gao  (高守婉).

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Conflict of Interest The authors declare that they have no conflict of interest.

Additional information

This work was supported by the National Natural Science Foundation of China under Grant No. 62272462, the Natural Science Foundation of Jiangsu Province of China for Distinguished Young Scholars under Grant No. BK20230045, and the Shenzhen Science and Technology Program under Grant No. JCYJ20230807154300002.

Peng-Peng Chen received his Ph.D. degree in computer application technology from Ocean University of China, Qingdao, in 2011. He is currently a professor at the School of Computer Science and Technology, China University of Mining and Technology, Xuzhou. His research interests include sensor networks, intelligent coal mines, and data modeling.

Kui-Yuan Zhang is currently pursuing his Ph.D. degree at the School of Computer Science and Technology, China University of Mining and Technology, Xuzhou. His research interests include signal processing, wireless sensing, and positioning technology.

Shou-Wan Gao received her Ph.D. degree in computer application technology from Ocean University of China, Qingdao, in 2011. She is currently an associate professor at the School of Computer Science and Technology, China University of Mining and Technology, Xuzhou. Her research interests include networked estimation, coal information systems, and the Internet of Things.

Si-Yi Ren is currently pursuing her Master’s degree at the School of Computer Science and Technology, China University of Mining and Technology, Xuzhou. Her research interests are the Internet of Things and simultaneous localization and mapping.

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Chen, PP., Zhang, KY., Gao, SW. et al. UAV Localization with Unreliable Observations in Hostile Underground Environments. J. Comput. Sci. Technol. 39, 1401–1418 (2024). https://doi.org/10.1007/s11390-024-3020-0

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  • DOI: https://doi.org/10.1007/s11390-024-3020-0

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