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Indoor tracking in WLAN location with TOA measurements
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Proceedings of the 4th ACM international workshop on Mobility management and wireless access table of contents
Terromolinos, Spain
POSTER SESSION: Posters table of contents
Pages: 121 - 125  
Year of Publication: 2006
ISBN:1-59593-488-X
Authors
Marc Ciurana  Universitat Politècnica de Catalunya, Barcelona, Spain
Francisco Barceló  Universitat Politècnica de Catalunya, Barcelona, Spain
Sebastiano Cugno  Universitat Politècnica de Catalunya, Barcelona, Spain
Sponsors
ACM: Association for Computing Machinery
SIGSIM: ACM Special Interest Group on Simulation and Modeling
Publisher
ACM  New York, NY, USA
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ABSTRACT

Authors presented recently an indoor location technique based on Time Of Arrival (TOA) obtained from Round-Trip-Time (RTT) measurements at data link level and trilateration. This new approach uses the existing IEEE 802.11 WLAN infrastructure with minor changes to provide an accurate estimation of the position of static wireless terminals. This paper presents advances on how to incorporate tracking capabilities to this approach in order to achieve a noticeable enhancement in the positioning accuracy while maintaining the computational cost low, both essential requirements in some critical applications of indoor pedestrian navigation in which people carrying light mobile devices has to be tracked with precision. Taking as a basis the Discrete Kalman Filter, customizations and optimizations have been designed and presented. Results obtained after conducting extensive simulations fed with actual ranging observables demonstrate the validity and suitability of the researched algorithms and its ability to provide very high performance level in terms of accuracy and robustness.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
F. Izquierdo, M. Ciurana, F. Barceló, J. Paradells, E. Zola "Performance evaluation of a TOA-based trilateration method to locate terminals in WLAN". Proc. IEEE ISWPC 2006, pp. 217--222.
 
2
B. Long Le, K. Ahmed, H. Tsuji "Mobile Location Estimator with NLOS Mitigation Using Kalman Filtering" Mar 17-19 2003, IEEE WCNC New Orleans, USA, 2003
 
3
N. J. Thomas, D. G. M. Cruickshank, D. I. Laurenson, "A robust Location Estimator Architecture with Biased Kalman Filtering of TOA Data for Wireless Systems" IEEE 6th Int. Symp. on Spread-Spectrum Tech & Appli NJIT, pp. 296--300, New Jersey, USA, Sept 6-8, 2000
 
4
M. Najar, J. Vidal, A. Kjellstrom. "Kalman Tracking for UMTS Mobile Location". Proc. IST Summit 2001, p. 230--235.
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7
J. Latvala, J. Syrjärinne, S. Niemi, J. Niittylahti, "Patient Tracking in a Hospital Environment Using Wireless Stations and Extended Kalman Filtering", Proceedings of the 1999 Middle East Conference on Networking.
 
8
F. Evennou and F. Marx ,"Improving Positioning capabilities for indoor environments with WiFi", IST Summit 2005.
 
9
I. Guvenc, C. T. Abdallah, R. Jordan, and O. Dedeoglu, "Enhancements to RSS based indoor tracking systems using Kalman filters," in Proc. of International Signal Processing Conference and Global Signal Processing Expo, 2003.
 
10
R. Kalman, "A new approach to linear filtering and prediction problems", Trans. ASME, J. Basic Eng. 82D, pp. 35--45, 1960.


Collaborative Colleagues:
Marc Ciurana: colleagues
Francisco Barceló: colleagues
Sebastiano Cugno: colleagues