NDLI: A Hybrid Neural Network-Data Base Correlation Positioning in GSM Network

Content Provider	IEEE Xplore Digital Library
Author	Takenga, C. Chen Xi Kyamakya, K.
Copyright Year	2006
Description	Author affiliation: Inst. of Commun. Eng., Hannover Univ. (Takenga, C.; Chen Xi)
Abstract	Mobile terminal (MT) localization in a GSM environment has been of big interest in the recent years. This work exploits the advantage of position estimations from different sources in a robust fusion algorithm to reduce the positioning error. A hybrid neural network (NN)-data base correlation method (DC) is discussed. Before the fusion process, the DC position estimates are post-processed using an extra NN in order to reduce its error. Function approximation and classification properties of the NN will be investigated and the best NN architecture will be applied in the positioning algorithm. Results show that, the post processing of the DC results has a big impact on the positioning accuracy and the fusion process gets the MT estimate within a better accuracy
Starting Page	1
Ending Page	5
File Size	374777
Page Count	5
File Format	PDF
ISBN	142440410X
DOI	10.1109/ICCS.2006.301534
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-30
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks GSM Databases Fingerprint recognition Correlation Time measurement Urban areas Intelligent networks Mobile communication Transportation neural network localization mobile positioning data fusion database correlation
Content Type	Text
Resource Type	Article

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