Wi-fi based indoor localization and tracking using sigma-point kalman filtering methods

Record - IEEE PLANS, Position Location and Navigation Symposium

Volumn , Issue , 2008, Pages 646-659

  Paul, Anindya S ^a   Wan, Eric A ^a  

^a OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

Author keywords

Indoor tracking; RSSI tracking; Sigma point Kalman filter; Sigma point Kalman smoother

Indexed keywords

BAYESIAN NETWORKS; CONTROL THEORY; COST EFFECTIVENESS; INFERENCE ENGINES; KALMAN FILTERS; NAVIGATION; SENSORS; SIGNAL RECEIVERS; UBIQUITOUS COMPUTING; WI-FI;

BAYESIAN INFERENCES; HUMAN WALKINGS; INDOOR ENVIRONMENTS; INDOOR LOCALIZATIONS; INDOOR TRACKING; IS COSTS; KALMAN FILTERING METHODS; KALMAN SMOOTHERS; LOCATION TRACKING; MOTION SENSORS; PREDICTIVE MODELS; RECEIVED SIGNAL STRENGTH INDICATIONS; RSSI TRACKING; SIGMA-POINT KALMAN FILTER; SIGMA-POINT KALMAN SMOOTHER; SIGNAL STRENGTHS; TRACKING ALGORITHMS;

SENSOR NETWORKS;

EID: 55349108885     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/PLANS.2008.4569985     Document Type: Conference Paper

References (38)

1. R. Want, A, Hopper, V. Falcao, and J. Gibbons, "The active badge location system," ACM Transactions on Information Systems, vol. 40, no. 1, pp. 91-102, January 1992.
2. P. Bahl and V. Padmanabhan, "Radar: An in-bulding rf-based user location and tracking system," in Proceedings of IEEE Infocomm 00, April 2000.
3. N. Priyantha, A. Charraborty, and H. Balakrishnan, "The cricket location-support system," in Proceedings of ACM Mobicomm 00, July 2000.
4. J. Hightower and G. Bordello, "Particle filters for location estimation in ubiquitious computing: A case study," in Proceedings of International Conference on Ubiquitious Computing (UBICOMP), 2004.
5. A. LaMarca, Y. Chawathe, S. Consolvo, J. Hightower, 1. Smith, J. Scott, T. Sohn, J. Howard, J. Hughes, F. Potter, J. Tabert5, P. Powledge, G. Bordello, and B. Schilit, "Place lab: Device positioning using radio beacons in the wild," in Proceedings of International Conference on Pervasive Computing (Pervasive), 2005.
6. Y. Cheng, Y Chawathe, A. LaMraca, and J. Krumm, "Accuracy characterization for metropolitan-scale wi-fi localization," in Proceedings of 3rd international conference on Mobile systems, applications, and services, 2005, pp. 233-245.
7. [Online]. Available: http://www.ekahau.com/
8. [Online]. Available: http://www.ubisense.net/
9. [Online]. Available: http://www.sonitor.com/
10. D. Fox, J. Hightower, L. Liao, D. Schulz, and G. Bordello, "Bayesian filtering for location estimation," IEEE pervasive computing, 2003.
11. A. Elfes, E. Prassler, and J. Scholz, "Tracking people in a railway station during rush hour," in Proceedings of computer vision system, ICVS99, 1999.
12. A. H. A. Fod and M. Mataric, "Laser-based people tracking," in Proceedings of the IEEE International Conference on Robotics & Automation, 2002.
13. C.-T Hsieh, C.-Y Chen, and Y.-K. Wu, "People tracking system using kalman filter," in Proceedings of Signal and image processing, 2002.
14. J. Krumm and E. Horvitz, "Locadio:inferring motion and location from wi-fi signal strengths," in Proceedings of International Conference onMobile and Ubiquitious Systems:Networking and Services(MobiQuitous 04), 2004.
15. F. Gustafsson, F. Gunnarsson, N. Bergman, U. Fors-sell, J. Jansson, R. Karlsson, and P. Nordlund, "Particle filters for positioning, navigation and tracking," IEEE Transactions on Signal Processing, vol. 50, pp. 425-435, 2002.
16. B. Ferris, D. Haehnel, and D. Fox, "Gaussian processes for signal strength-based location estimation," Proc. of Robotics: Science and Systems, 2006, 2006.
17. J. Letchner, D. Fox, and A. LaMarce, "Large-scale localization from wireless signal strength," Proceeding of the National Conference on Artificial Intelligence (AAAI-05), 2005.
18. D. Hhnel, W. Burgard, D. Fox, K. Fishkin, and M. Phili-pose, "Mapping and localization with rfid technology," in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2004.
19. F. Lewis, Optimal Estimation with an Introduction to Stochastic Control Theory. John Wiley and Sons, 1986.
20. Q. Meng, Y Sun, and Z. Cao, "Adaptive extended kalman filter (aekf)-based mobile robot localization using sonar," Robotica, vol. 18, pp. 459-473, 2000.
21. J. Leonard and H. Durrant-Whyte, "Mobile robot localization by tracking geometric beacons," IEEE Transactions on Robotics and Automation, vol. 7, no. 3, pp. 376-382, June 1991.
22. S. Borthwick, M. Stevens, and H. Durrant-Whyte, "Position estimation and tracking using optical range data," in Proceedings of the IEEE International Conference on Intelligent Robots and Systems, 1993.
23. S, Seidal and T. Rappaport, "914 mhz path loss prediction models for indoor wireless communications in multifloored buildings," IEEE Transactions on Antennas and Propagation, vol. 40(2), 1992.
24. E. A. Wan and R. van der Merwe, "The unscented kalman filter for nonlinear estimation," in Proceedings of Symposium 2000 on Adaptive Systems for Signal Processing, Communication and Control (AS-SPCC), October 2000.
25. E. Wan and R. van der Merwe, Kalman Filtering and Neural Networks, 1st ed. John Wiley & Sons, 2001, ch. 7, pp. 221-280.
26. R. van der Merwe and E. Wan, "Sigma-point kalman filters for probabilistic inference in dynamic state-space models," in Proceedings of Workshop on Advances in Machine Learning, June 2003.
27. S. Julier, J. Uhlmann, and H. Durrant-Whyte, "A new approach for filtering nonlinear systems," in Proceedings of American Control Conference, 1995, pp. 1628-1632.
28. M. Norgaard, N, Poulsen, and O. Ravn, "New developments in state estimation for nonlinear systems," Auto-tnatica, vol. 36, pp. 1627-1638, November 2000.
29. R. van der Merwe and E. A. Wan, 'The square-root unscented kalman filter for state and parameter estimation," in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 6, Salt Lake City, Utah, May 2001, pp. 3461-3464.
30. A. S. Paul and E. A. Wan, "A new formulation for nonlinear forward-backward smoothing," in Proceedings of International Conference on Acoustics, Speech and Signal Processing(ICASSP), 2008.
31. S. Haykin, Neural networks : a comprehensive foundation. Maxwell Macmillan International, 1994.
32. R. Kalman, "A new approach to linear filtering and prediction problems," Trnsactions of the ASME, Journal of Basic Engineering, vol. 82, pp. 35-45, March 1960.
33. D. Simon, Optimal State Estimation. Wiley Inter-Science, 2006.
34. D. Fraser and J. Potter, "The optimum linear smoother as a combination of two optimum linear filters," IEEE Transactions on Automatic Control, vol. 14, no. 4, pp. 387-390, 1969.
35. H. Rauch, F. Tung, and C. Striebel, "Maximum likelihood estimates of linear dynamic systems," AIAA, vol. 3, no. 8, pp. 1445-1450, 1965.
36. C. Leondes, J. Peller, and E. Stear, "Nonliner smoothing theory," IEEE Transactions on System., Science and Cybernetics, vol. 6, no. 1, January 1970.
37. S. J. Julier and J. K. Uhlmann, "Unscented filtering and nonlinear estimation," Proceedings of the IEEE, vol. 92, pp. 401-422, March 2004.
38. R. van der Merwe, "Sigma-point kalman filters for probabilistic inference in dynamic state-space models," Ph.D. dissertation, OGI School of Science and Engineering at Oregon Health & Science University (OHSU), 2004.