Browse > Article

An Improvement for Location Accuracy Algorithm of Moving Indoor Objects  

Kim, Mi-Kyeong (한밭대학교 정보통신대학원 정보통신공학과)
Jeon, Hyeon-Sig (한밭대학교 정보통신전문대학원 전파공학과)
Yeom, Jin-Young (한밭대학교 정보통신전문대학원 전파공학과)
Park, Hyun-Ju (한밭대학교 정보통신컴퓨터공학부)
Publication Information
Journal of Internet Computing and Services / v.11, no.2, 2010 , pp. 61-72 More about this Journal
Abstract
This paper addresses the problem of moving object localization using Ultra-Wide-Band(UWB) range measurement and the method of location accuracy improvement of the indoor moving object. Unlike outdoor environment, it is difficult to track moving object position due to various noises in indoor. UWB is a radio technology that has attention for localization applications recently. UWB's ranging technique offer the cm accuracy. Its capabilities for data transmission, range accurate estimation and material penetration are suitable technology for indoor positioning application. This paper propose a positioning algorithm of an moving object using UWB ranging technique and particle filter. Existing positioning algorithms eliminate estimation errors and bias after location estimation of mobile object. But in this paper, the proposed algorithm is that eliminate predictable UWB range distance error first and then estimate the moving object's position. This paper shows that the proposed positioning algorithm is more accurate than existing location algorithms through experiments. In this study, the position of moving object is estimated after the triangulation and eliminating the bias and the ranging error from estimation range between three fixed known anchors and a mobile object using UWB. Finally, a particle filter is used to improve on accuracy of mobile object positioning. The results of experiment show that the proposed localization scheme is more precise under the indoor.
Keywords
UWB; Particle Filter; Indoor Location Tracking; TOA; Triangulation; NLOS;
Citations & Related Records
연도 인용수 순위
  • Reference
1 P. Del Moral and L. Miclo. Branching and interacting particle systems approximations of Feynman -Kac formulae with applications to non linear filtering. In Seminaire de Probabilites XXXIV, number 1729 in Lecture Notes in Mathematics. Springer-Verlag, 2000.
2 A. Doucet, S.J. Godsill, and C. Andrieu. On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 2000.
3 D. Fox, W. Burgard, F. Dellaert, and S. Thrun. Monte Carlo Localization: Efficient position estimation for mobile robots. In Proc. of the National Conference on Artificial Intelligence, 1999.
4 Mohammed M. Olama, Seddik M. Djouadi, Ioannis G. Papageorgiou, and Charalambos D. Charalambous, Position and Velocity Tracking in Mobile Networks Using Particle and Kalman Filtering With Comparison. IEEE Transaction On Vehicular Technology, Vol. 57, No. 2, March. 2008
5 D. Fox, S. Thrun, F. Dellaert, and W. Burgard. Particle filters for mobile robot localization.
6 P. Jensfelt, O. Wijk, D. Austin, and M. Andersson. Feature based condensation for mobile robot localization. In Proc. of the IEEE International Conference on Robotics & Automation, 2000.
7 Damien B. Jourdan, John J. Deyst, Jr., Moe Z. Win, Nicholas Roy. Monte Carlo Localization in Dense Multipath Environments Using UWB Ranging. IEEE International Conference on Ultra-Wideband, 2005
8 Greg Welch and Gary Bishop. An Introduction to the Kalman Filter. 95-041 Department of Computer Science University of North Carolina at Chapel Hill, NC 27599-3175 Updated: Monday, July 24, 2006
9 S.J. Julier and J.K. Uhlmann. A new extension of the Kalman filter to nonlinear systems. In Proc. of AeroSense: The 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls, 1997
10 R. E. Kalman. A new approach to linear filtering and prediction problems. Trans. of the ASME, Journal of basic engineering, 82:35-45, March 1960.   DOI
11 D. Koller and R. Fratkina. Using learning for approximation in stochastic processes. In Proc. of the International Conference on Machine Learning, 1998.
12 Zhao Dong-ming. Application of Unscented Kalman Filter for Non-linear Estimation in Deformation Monitoring. 3rd IAG 12th FIG Symposium, Baden, May 22-24 2006
13 E.A.Wan and R. van der Merwe. The unscented Kalman filter for nonlinear estimation. In Proc. of Symposium 2000 on Adaptive Systems for Signal Processing, Communications, and Control, 2000.
14 Ioannis M. Rekleitis. A Particle Filter Tutorial for Mobile Robot Localization TR-CIM-04-02.
15 Dieter Fox. Adapting the Sample Size in Particle Filters Through KLD-Sampling. International Journal of Robotics Research, 2003
16 이정석, 정완균. 동적 환경에서 파티클 필터 를 이용한 로봇의 강인한 위치추적 알고리 즘. 제2회 한국로봇공학회 하계종합 학술대회 논문집, 2007
17 J. Gonzalez et al. Mobile robot localization based on Ultra-Wide-Band ranging: A particle filter approach. Journal of Cleaner Production Volues 16, Issue 16, November 2008, Pages 1741-1754   DOI   ScienceOn
18 M. Sanjeev Arulampalam, Simon Maskell, Neil Gordon, and Tim Clapp, A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on signal processing, 2002
19 F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P-J. Nordlund. Particle filters for positioning, navigation and tracking. IEEE Transactions on Signal Processing, 2002.
20 Nayef Alsindi and Kaveh Pahlavan. Coorative Localization Bounds for Indoor Ultra-Wideban Wireless Sensor Networks. EURASIP Journal on Advances in Signal Processing Volume 2008, Article ID 852509
21 S. Roy, J.R. Foerster, and V.S. Somayazulu. Ultrawideband radio design: The promise of high-speed short-range wireless connectivity. IEEE Proc. vol.92, no.2, pp.295-311, 2004.   DOI   ScienceOn