A modular classification model for received signal strength based location systems

Neurocomputing

Volumn 71, Issue 13-15, 2008, Pages 2657-2669

  Ahmad, Uzair ^a   Gavrilov, Andrey V ^a   Lee, Sungyoung ^a   Lee, Young Koo ^a  

^a Kyung Hee University   (South Korea)

Author keywords

Indoor positioning systems; Location estimation; Modular classifiers; Received signal strength; Visibility matrix

Indexed keywords

INDOOR POSITIONING SYSTEMS; VISIBILITY;

CLASSIFICATION METHODS; CLASSIFICATION MODELS; ENVIRONMENTAL FACTORS; LOCATION ESTIMATION; MODULAR CLASSIFIERS; REAL WORLD ENVIRONMENTS; RECEIVED SIGNAL STRENGTH; VISIBILITY MATRIX;

LOCATION;

ACCURACY; CLASSIFICATION; CONFERENCE PAPER; ENVIRONMENTAL FACTOR; MOBILE PHONE; PRIORITY JOURNAL; PROBABILITY; SIGNAL DETECTION; STATISTICAL MODEL; TELECOMMUNICATION;

EID: 56449107639     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.11.045     Document Type: Conference Paper

References (32)

1. U. Ahmad, et al., In-building localization using neural networks, in: IEEE International Conference on Engineering of Intelligent Systems, Islamabad, Pakistan, March 2006.
2. U. Ahmad, et al., Modular multilayer perceptron for WLAN based localization, in: IEEE International Joint Conference on Neural Networks, Vancouver, Canada, July 2006.
3. U. Ahmad, et al., Location-ware: A distributed wireless site calibration system for location awareness, in: The 2006 IFIP International Conference on Embedded and Ubiquitous Computing, Seuol, Korea, August 2006.
4. P. Bahl, et al. RADAR: An in-building RF-based user location and tracking system, in: IEEE INFOCOM 2000, March 2000, pp. 775-784.
5. R. Battiti, A. Villani, T. Le Nhat, Neural network model for intelligent networks: Deriving the location from signal patterns, in: The First Annual Symposium on Autonomous Intelligent Networks and Systems 2002.
6. C.C. Chiang, A divide-and-conquer methodology for modular supervised neural network desien, in: Proceedings of 1994 International Joint Conference on Neural Networks, pp. 119-124.
7. S.-B. Cho, Combining multiple neural networks by fuzzy integral for robust classification, IEEE Trans. Syst. Man Cybern. 25 (2) (1995).
8. CompoNet: Componentization of Neural Networks (http://www.http://sourceforge.net/projects/componet/).
9. F. Dieter, et al., Bayesian filters for location estimation, in: IEEE CS and IEEE ComSoc 1536-1268/03.
10. C. Ding, H. Peng, Minimum redundancy feature selection for gene expression data, in: Proceedings of the IEEE Computer Society Bioinformatics Conference (CSB 2003), Stanford, CA, August 2003, pp. 523-529.
11. S.-U. Guan, et al., Neural network task decomposition based on output partitioning, Journal of the Institution of Engineers, Singapore 44 (3) (2004).
12. I. Guvenc, et al., Enhancements to RSS based indoor tracking systems using kalman filters, in: GSPx International Signal Processing Conference, Dallas, TX, March 31-April 3, 2003.
13. M.T. Hagan, M. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Trans. Neural Networks 5 (6) (1994) 989-993.
14. J.B. Hampshire, et al., The Meta-Pi network: Building distributed knowledge representations for robust multisource pattern recognition, IEEE Trans. Pattern Anal. Mach. Intell. 14 (7) (1992).
15. T.K. Ho, Decision combination in multiple classifier systems, IEEE Trans. Pattern Anal. Mach. Intell. 16 (I) (1994) 66-75.
16. K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural Networks 4 (1991) 251-257.
17. A.K. Jain, B. Chandrasekaran, Dimensionality and sample size considerations in pattern recognition practice, in: Handbook of Statistics, vol. 2, North-Holland, Amsterdam, 1987, pp. 835-855.
18. J. Kittler, et al., On combining classifiers, IEEE Trans. Pattern Anal. Mach. Intell. 20 (3) (1998).
19. T. Kohonen, Learning vector quantization, in: The Handbook of Brain Theory and Neural Networks, MIT Press, Cambridge, MA, 1995, pp. 537-540.
20. A.M. Ladd, et al., Using wireless ethernet for localization, in: IEEE/ RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland, September 2002.
21. MATLAB Neural Network Toolbox (http://www.mathworks.com/products/ neuralnet/).
22. M.F. Moller, A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks 6 (1993) 525-533.
23. T. Ogawa, S. Yoshino, M. Shimizu, Location determination method for wireless systems based on LVQ, NTT Network Innovation Laboratories, vol. 1, no. 9, Japan.
24. K. Pehlavan, et al., Indoor geolocation science and technology, IEEE Communications Magazine 40 (2) (2002) 112-118.
25. N.B. Priyantha, A. Chakraborty, H. Balakrishnan, The cricket location-support system, in: Proceedings of the Sixth ACM MOBICOM, Boston, MA, August 2000.
26. Steve Pope, "Issues related to RSSI measurement, IEEE 802.11-02/520r0 (www.grouper.ieee.org/groups/802/11/Minutes/ Cons_Minutes_Nov-2003.pdf).
27. M. Sarkar, Modular pattern classifiers: A brief survey, in: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, 2000, pp. 2878-2883.
28. Smailagic, et al., Location sensing and privacy in a context aware computing environment, Pervasive Computing, 2001.
29. F. Soulie, Multi-modular neural network-hybrid architectures: A review, in: Proceedings of 1993 International Joint Conference on Neural Networks, pp: 2231-2236.
30. R. Want, et al., The active badge location system, ACM Trans. Inf. Syst. 10 (1) (1992) 91-102.
31. A. Ward, et al., A new location technique for the active office, IEEE Pers. Commun. 4 (5) (1997) 42-47.
32. L. Xu, et al., Methods of combining multiple classifiers and their applications to handwriting recognition, IEEE Trans. Syst. Man Cybern. 22 (3) (1992).