A Study of UWB Placement Optimization Based on Genetic Algorithm
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초록
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 highrise 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.
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 highrise 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.
참고문헌 (29)
- Baldi, P., "Modeling and optimization of UWB communication networks through a flexible cost function", IEEE Journal on Selected Areas in Communications, 20, pp.1733~1744 (2002)[DOI]
- Bhondekar, A., "Genetic Algorithm Based Node Placement Methodology for Wireless Sensor Networks", Proceedings of the international multiconference of engineers and computer scientists (2009)
- 최자성, "드론택시(UAM)의 수직이착륙장(Vertiport) 설치기준 연구", 한국항공운항학회지, 29(1), pp.74~81 (2021), 한국항공운항학회[DOI]
- Dai, H. F., "An INS", GNSS integrated navigation in GNSS denied environment using recurrent neural network, 2020, pp.16, 334~340
- Duan, S., "Ultra-Wideband Radio Channel Characteristics for Near-Ground Swarm Robots Communication", IEEE Transactions on Wireless Communications, 19, pp.4715~4726 (2020)[DOI]
- Fan, J., "Using genetic algorithms to optimise Wireless Sensor Network design", PhD, pp.2009
- Ferrero-Guillén, R., "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, pp.273~283 (2020)
- Gigl, T., "Analysis of a UWB Indoor Positioning System Based on Received Signal Strength", 4th Workshop on Positioning, Navigation and Communication (2007)
- He, R., "Analysis of the Relation Between Fresnel Zone and Path Loss Exponent Based on Two-Ray Model", IEEE Antennas and Wireless Propagation Letters, 11, pp.208~211 (2012)[DOI]
- Hussain, A., "Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator", Computational intelligence and neuroscience (2017)[DOI]
- Johansson, C., "Optimizing Genetic Algorithms for Time Critical Problems", M.S., pp.2003
- Kim, E.-H., "A UWB positioning network enabling unmanned aircraft systems auto land", Aerospace Science and Technology, 58, pp.418~426 (2016)[DOI]
- 김진수, "무선 센서 네트워크에서 유전 알고리즘 기반의 에너지 효율적인 클러스터링", 한국산학기술학회논문지, 11(5), pp.1661~1669 (2010), 한국산학기술학회
- 김광백, "유전자 알고리즘을 이용한 경로 탐색", 한국정보통신학회논문지, 15(6), pp.1251~1255 (2011), 한국정보통신학회
- 이신해, "유전자 알고리즘을 이용한 대중교통 통행배정모형 개발", 대한교통학회지, 21(1), pp.65~75 (2003), 대한교통학회
- 오동익, "유전 알고리즘을 이용한 Wibro MMR 네트워크의 최적 배치 탐색", 대한산업공학회지, 34(2), pp.235~245 (2008), 대한산업공학회
- 박지호, "위성항법시스템과 비전시스템 융합 기술 기반의 신뢰성있는 위치 측위에 관한 연구", 전자공학회논문지 - TC, 48(10), pp.20~28 (2011), 대한전자공학회
- 박지원, "다중경로 환경에서의 TOA방식과 TDOA방식의 측위성능 비교", 전기학회논문지ABCD, 58(2), pp.415~421 (2009), 대한전기학회
- Peng, B., "An improved localization algorithm based on genetic algorithm in wireless sensor networks", Cognitive Neurodynamics, 9, pp.249~256 (2015)[DOI]
- Promwong, S., "Theoretical ground reflection model for UWB communication systems", IEEE International Symposium on Communications and Information Technology (2004)
- Pukhova, A., "Flying taxis revived: Can Urban air mobility reduce road congestion?", Journal of Urban Mobility, 1, pp.100002~ (2021)[DOI]
- Queralta, J. P., "UWB-based System for UAV Localization in GNSS-Denied Environments: Characterization and Dataset", 2020 IEEE, 2020, ~
- Shin, D. H., "Comparisons of position error characteristics and DOP between TOA and TDOA technique", Journal of Institute of Control, Robotics and Systems, 6, pp.923~927 (2000)
- Sivakumar, S., "Error Minimization in Localization of Wireless Sensor Networks using Genetic Algorithm", International Journal of Computer Applications, 43, pp.16~20 (2012)[DOI]
- Xia, B., "UWB Positioning System Based on Genetic Algorithm", Journal of Computer and Communications, 9, pp.110~118 (2021)[DOI]
- 윤유림, "최대 커버리지 센서 배치를 위한 유전 알고리즘", 한국지능시스템학회 논문지, 20(3), pp.406~412 (2010), 한국지능시스템학회
- Yun, Z., "An Integrated Method of Ray Tracing and Genetic Algorithm for Optimizing Coverage in Indoor Wireless Networks", IEEE Antennas and Wireless Propagation Letters, 7, pp.145~148 (2008)
- Zhang, Q., "Genetic Algorithm Based Wireless Sensor Network Localization", Fourth International Conference on Natural Computation (2008)
- Zhang, Y. J., "Node Placement Optimization of Wireless Sensor Networks Using Multi-Objective Adaptive Degressive Ary Number Encoded Genetic Algorithm", Algorithms, 13, pp.189~ (2020)[DOI]