Browse > Article
http://dx.doi.org/10.11003/JPNT.2022.11.2.99

A Study of UWB Placement Optimization Based on Genetic Algorithm  

Jung, Doyeon (Department of Mechanical Engineering, Hongik University)
Kim, Euiho (Department of Mechanical & System Design Engineering, Hongik University)
Publication Information
Journal of Positioning, Navigation, and Timing / v.11, no.2, 2022 , pp. 99-107 More about this Journal
Abstract
Urban Air Mobility (UAM) such as a drone taxi is one of the future transportations that have recently been attracting attention. Along with the construction of an urban terminal, an accurate landing system for UAM is also essential. However, in urban environments, reliable Global Navigation Satellite Systems (GNSS) signals cannot be received due to obstacles such as high-rise buildings which causes multipath and non-line of sight signal. Thus, the positioning result in urban environments from the GNSS signal is unreliable. Consequently, we propose the Ultra-Wideband (UWB) network to assist the soft landing of UAM on a vertiport. Since the positioning performance of UWB network depends on the layout of UWB anchors, it is necessary to optimize the layout of UWB anchors. In this paper, we propose a two-steps genetic algorithm that consists of binary genetic algorithm involved multi objectives fitness function and integer genetic algorithm involved robust solution searching fitness function in order to optimize taking into account Fresnel hole effects.
Keywords
genetic algorithm; UWB; Fresnel hole; vertiport; positioning accuracy;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Gigl, T., Janssen, G. J. M., Dizdarevic, V., Witrisal, K., & Irahhauten, Z. 2007, Analysis of a UWB Indoor Positioning System Based on Received Signal Strength, in 4th Workshop on Positioning, Navigation and Communication, Hannover, Germany, 22-22 Mar 2007 https://doi.org/10.1109/WPNC.2007.353618   DOI
2 Bhondekar, A., Renu, V., Singla, M., Ghanshyam, C., & Pawan, K. 2009, Genetic Algorithm Based Node Placement Methodology for Wireless Sensor Networks, in Proceedings of the international multiconference of engineers and computer scientists, Hong Kong, 18-20 Mar 2009.
3 Duan, S., Su, R., Xu, C., Chen, Y., & He, J. 2020, Ultra Wideband Radio Channel Characteristics for Near Ground Swarm Robots Communication, IEEE Transactions on Wireless Communications, 19, 4715-4726. https://doi.org/10.1109/TWC.2020.2986446   DOI
4 Ferrero-Guillen, R., Diez-Gonzalez, J., Alvarez, R., & Perez, H. 2020, Analysis of the Genetic Algorithm Operators for the Node Location Problem in Local Positioning Systems, Lecture Notes in Computer Science Hybrid Artificial Intelligent Systems, 12344, 273-283. https://doi.org/10.1007/978-3-030-61705-9_23   DOI
5 Hussain, A., Muhammad, Y. S., Nauman Sajid, M., Hussain, I., Mohamd Shoukry, A., et al. 2017, Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator, Computational intelligence and neuroscience, Article ID 7430125. https://doi.org/10.1155/2017/7430125
6 Kim, J.-S. 2010, An Energy Efficient Clustering based on Genetic Algorithm in Wireless Sensor Networks, Journal of the Korea Academia-Industrial cooperation Society, 11, 1661-1669. https://doi.org/10.5762/KAIS.2010.11.5.1661   DOI
7 Oh, D. & Kim, W. J. 2008, Optimal topology in Wibro MMR Network Using a Genetic Algorithm, Journal of Korean Institute of Industrial Engineers, 34, 235-245.
8 Queralta, J. P., Almansa, M. C., Schiano, F., Floreano, D., & Westerlund, T., 2020, UWB-based System for UAV Localization in GNSS-Denied Environments: Characterization and Dataset, in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, 24 Oct 2020-24 Jan 2021. https://doi.org/10.1109/IROS45743.2020.9341042   DOI
9 Peng, B. & Li, L. 2015, An improved localization algorithm based on genetic algorithm in wireless sensor networks, Cognitive Neurodynamics, 9, 249-256. https://doi.org/10.1007/s11571-014-9324-y   DOI
10 Baldi, P., De Nardis, L., & Di Benedetto, M. G. 2002, Modeling and optimization of UWB communication networks through a flexible cost function, IEEE Journal on Selected Areas in Communications, 20, 1733-1744. https://doi.org/10.1109/JSAC.2002.805619   DOI
11 Johansson, C. & Evertsson, G. 2003, Optimizing Genetic Algorithms for Time Critical Problems, M.S. Dissertation, Department of Software Engineering and Computer Science Blekinge Institute of Technology, Ronneby, Sweden.
12 Choi, J. S., Lee, S. H., Baek, J. S., & Hwang, H. W. 2021, A study on vertiport installation standard of drone taxis (UAM), Journal of the Korean Society for Aviation and Aeronautics, 29, 74-81. https://doi.org/10.12985/ksaa.2021.29.1.074   DOI
13 Dai, H. F., Bian, H. W., Wang, R. Y., & Ma, H. 2020, An INS/GNSS integrated navigation in GNSS denied environment using recurrent neural network, Defence Technology, 16, 334-340. https://doi.org/10.1016/j.dt.2019.08.011   DOI
14 He, R., Zhong, Z., Ai, B., Ding, J., & Guan, K. 2012, Analysis of the Relation Between Fresnel Zone and Path Loss Exponent Based on Two-Ray Model, IEEE Antennas and Wireless Propagation Letters, 11, 208-211. https://doi.org/10.1109/LAWP.2012.2187270   DOI
15 Kim, E.-H. & Choi, D.-K. 2016, A UWB positioning network enabling unmanned aircraft systems auto land, Aerospace Science and Technology, 58, 418-426. https://doi.org/10.1016/j.ast.2016.09.005   DOI
16 Kim, K.-B. & Song, D.-H. 2011, Path Search Method using Genetic Algorithm. Journal of the Korea Institute of Information and Communication Engineering, 15, 1251-1255. https://doi.org/10.6109/JKIICE.2011.15.6.1251   DOI
17 Sivakumar, S., & Venkatesan, R., & Karthiga, M. 2012, Error Minimization in Localization of Wireless Sensor Networks using Genetic Algorithm, International Journal of Computer Applications, 43, 16-20. https://doi.org/10.5120/6155-8547   DOI
18 Xia, B., Zheng, X., Zhang, L., & Zhao, L. 2021, UWB Positioning System Based on Genetic Algorithm, Journal of Computer and Communications, 9, 110-118. https://doi.org/10.4236/jcc.2021.94008   DOI
19 Yun, Z., Lim, S., & Iskander, M. F. 2008, An Integrated Method of Ray Tracing and Genetic Algorithm for Optimizing Coverage in Indoor Wireless Networks, in IEEE Antennas and Wireless Propagation Letters, 7, 145-148. https://doi.org/10.1109/LAWP.2008.919358   DOI
20 Yoon, Y.-R. & Kim, Y.-H. 2010, Genetic Algorithms for Maximizing the Coverage of Sensor Deployment, Journal of The Korean Institute of Intelligent Systems, 20, 406-412. https://doi.org/10.5391/JKIIS.2010.20.3.406   DOI
21 Zhang, Q., Wang, J., Jin, C., Ye, J., Ma, C., et al. 2008, Genetic Algorithm Based Wireless Sensor Network Localization, in Fourth International Conference on Natural Computation, Jinan, China, 18-20 Oct 2008. https://doi.org/10.1109/ICNC.2008.206   DOI
22 Zhang, Y. J. & Liu, M. 2020, Node Placement Optimization of Wireless Sensor Networks Using Multi-Objective Adaptive Degressive Ary Number Encoded Genetic Algorithm, Algorithms, 13, 189. https://doi.org/10.3390/a13080189   DOI
23 Lee, S.-H., Choi, I.-J., Lee, S.-J., & Lim, K.-W. 2003, A Transit Assignment Model using Genetic Algorithm, Journal of Korean Society of Transportation, 21, 65-75.
24 Pukhova, A., Llorca, C., Moreno, A., Staves, C., Zhang, Q., et al. 2021, Flying taxis revived: Can Urban air mobility reduce road congestion?, Journal of Urban Mobility, 1, 100002. https://doi.org/10.1016/j.urbmob.2021.100002   DOI
25 Park, C.-H., Kwon, S., Lee, C.-H., & Jung, W.-Y. 2011, A study of a reliable positioning based on technology convergence of a satellite navigation system and a vision system, Journal of the Institute of Electronics Engineers of Korea TC, 48, 20-28.
26 Park, J.-W., Park, J.-H., Song, S.-H., & Sung, T.-K. 2009, Comparisons of Error Characteristics between TOA and TDOA Positioning in Dense Multipath Environment, The Transactions of The Korean Institute of Electrical Engineers, 58, 415-421.
27 Promwong, S., Takada, J., Supanakoon, P., & Tangtisanon, P. 2004, Theoretical ground reflection model for UWB communication systems, in IEEE International Symposium on Communications and Information Technology, Sapporo, Japan, 26-29 Oct 2004 https://doi.org/10.1109/ISCIT.2004.1413910   DOI
28 Fan, J. 2009, Using genetic algorithms to optimise Wireless Sensor Network design, PhD Dissertation, Loughborough University, UK.
29 Shin, D. H. & Sung, T. K. 2000, Comparisons of position error characteristics and DOP between TOA and TDOA technique, Journal of Institute of Control, Robotics and Systems, 6, 923-927.