Abstract
Telecom localization that had aroused widespread attentions of major telecommunication operators has become vital in recent years. However, current available technologies suffer from high localization errors, typically with mean errors more than 100 m. In order to tackle this problem, in this paper we leverage context knowledge to reduce the localization error. To this end, we propose a framework adopting several modified filter methods in terms of context to eliminate localization errors that cannot be easily detected by the existing localization algorithms. We apply the optimized filter methods combining with the context knowledge to verify the effectiveness of our methodologies according to the experiments based on the telecom localization utilizing the GPS-associated MR data in the downtown area of Shanghai, China.
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Acknowledgements
The authors would like to appreciate Professor Weixiong Rao for his critical and useful suggestions. The authors also want to thank two anonymous reviewers for their critics and suggestions that help improving the quality of our paper.
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Zhu, M., Cao, B., Yuan, M., Zeng, J. (2017). Correction of Telecom Localization Errors by Context Knowledge. In: Song, S., Renz, M., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10612. Springer, Cham. https://doi.org/10.1007/978-3-319-69781-9_11
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DOI: https://doi.org/10.1007/978-3-319-69781-9_11
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