Autonomous Control System with Passive Positioning for Unmanned-Aerial-Vehicle-Assisted Edge Communication in 6G
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Published:2023-10-06
Issue:19
Volume:13
Page:11014
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Hu Yue12, Jiang Yunzhe3, Liu Yinqiu4ORCID, He Xiaoming5
Affiliation:
1. College of Computer and Information, Hohai University, Nanjing 210024, China 2. NARI Technology Co., Ltd., Nanjing 210047, China 3. School of Communications and Information Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China 4. School of Computer Science and Engineering, Nanyang Technological University, Singapore 637121, Singapore 5. College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210000, China
Abstract
UAVs can be deployed in many scenarios to provide various types of services via 6G edge communication. In these scenarios, it is necessary to obtain the position of the UAVs in a timely and accurate manner to avoid UAV collisions. In this paper, we consider improved passive localization algorithms aimed at reducing convergence time and adapting to extreme conditions. For the sake of reducing the complexity of signals and ensuring the reliability of receiving processes, we reconsidered the angle between arrival signals as the feature in positioning. Then, according to the characteristics of the positioning process, we draw on the cyclical process of the iterative greedy algorithm to construct the coding, destruction, and reorganization process to guide the movement of the UAV. Moreover, an improved Metropolis criterion is added to prevent falling into the local optimal solution. Finally, the proposed algorithm is verified in the simulation results. The results show that the algorithm can achieve precise positioning and excellent track planning within a small number of iterations, and it reduces the amount of information carried by the signal and convergence time compared with the traditional method.
Funder
National Natural Science Foundation of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference20 articles.
1. Yang, X., Lin, D., Zhang, F., Song, T., and Jiang, T. (2019, January 11–13). High Accuracy Active Stand-off Target Geolocation Using UAV Platform. Proceedings of the 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), Chongqing, China. 2. Wang, S., Han, Y., Chen, J., Zhang, Z., Wang, G., and Du, N. (2018, January 10–12). A Deep-Learning-Based Sea Search and Rescue Algorithm by UAV Remote Sensing. Proceedings of the 2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC), Xiamen, China. 3. Zhang, W., Zhang, Q., Zhang, T., Zhao, Q., and Fu, S. (2021, January 1–4). Research on Interference Coupling Mechanism and Test Method of UAV in Complex Electromagnetic Environment. Proceedings of the 2021 13th International Symposium on Antennas, Propagation and EM Theory (ISAPE), Zhuhai, China. 4. Zhu, L., Xu, Z., and Wang, Y. (2021, January 3–7). Research on UAV route optimization in complex terrains. Proceedings of the 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), San Antonio, TX, USA. 5. Huang, H., Zhou, H., Zheng, M., Xu, C., Zhang, X., and Xiong, W. (2019, January 22–24). Cooperative Collision Avoidance Method for Multi-UAV Based on Kalman Filter and Model Predictive Control. Proceedings of the 2019 IEEE International Conference on Unmanned Systems and Artificial Intelligence (ICUSAI), Xi’an, China.
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