Abstract
Telecommunication (Telco) localization is an important complementary technique of Global Position System (GPS). Traditional Telco localization approaches requires radio signal strength indicator (RSSI) of mobile devices with the connected base stations (BSs). Unfortunately, many of real-world signal measurement could miss RSSI values, and Telco operators typically will not record RSSI information, e.g., due to the major departure from current operational practices of Telco operators [6]. To address this problem, we design a novel BS ID-based coarse-to-fine Telco localization model, namely BSLoc, which requires only the connected BS IDs, time and speed information of mobile devices. BSLoc consists of two layers: (1) a sequence localization model via Hidden Markov Model (HMM) to localize the mobile devices with coarse-grained locations, and (2) a machine learning regression model with engineered features to acquire the fine-grained locations of mobile devices. Our experiments verify that, on a 2G dataset, BSLoc achieves a median error 26.0 m, which is almost comparable with two state-of-art RSSI-based techniques [9] 17.0 m and [20] 20.3 m.
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Acknowledgment
This work is partially supported by National Natural Science Foundation of China (Grant No. 61572365, 61503286, 61702372) and sponsored by The Fundamental Research Funds for the Central Universities.
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Lv, J. et al. (2019). BSLoc: Base Station ID-Based Telco Outdoor Localization. In: Gilbert, S., Hughes, D., Krishnamachari, B. (eds) Algorithms for Sensor Systems. ALGOSENSORS 2018. Lecture Notes in Computer Science(), vol 11410. Springer, Cham. https://doi.org/10.1007/978-3-030-14094-6_14
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DOI: https://doi.org/10.1007/978-3-030-14094-6_14
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