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http://dx.doi.org/10.3837/tiis.2019.09.005

Hybrid Indoor Position Estimation using K-NN and MinMax  

Subhan, Fazli (Department of Computer Science, National University of Modern Languages NUML)
Ahmed, Shakeel (Department of Computer Science, National University of Modern Languages NUML)
Haider, Sajjad (Department of Computer Science, National University of Modern Languages NUML)
Saleem, Sajid (Department of Computer Science, National University of Modern Languages NUML)
Khan, Asfandyar (Institute of Business and Management Sciences, University of Agriculture)
Ahmed, Salman (Department of Computer and System Engineering, University of Engineering and Technology)
Numan, Muhammad (Department of Computer Science, National University of Modern Languages NUML)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.9, 2019 , pp. 4408-4428 More about this Journal
Abstract
Due to the rapid advancement in smart phones, numerous new specifications are developed for variety of applications ranging from health monitoring to navigations and tracking. The word indoor navigation means location identification, however, where GPS signals are not available, accurate indoor localization is a challenging task due to variation in the received signals which directly affect distance estimation process. This paper proposes a hybrid approach which integrates fingerprinting based K-Nearest Neighbors (K-NN) and lateration based MinMax position estimation technique. The novel idea behind this hybrid approach is to use Euclidian distance formulation for distance estimates instead of indoor radio channel modeling which is used to convert the received signal to distance estimates. Due to unpredictable behavior of the received signal, modeling indoor environment for distance estimates is a challenging task which ultimately results in distance estimation error and hence affects position estimation process. Our proposed idea is indoor position estimation technique using Bluetooth enabled smart phones which is independent of the radio channels. Experimental results conclude that, our proposed hybrid approach performs better in terms of mean error compared to Trilateration, MinMax, K-NN, and existing Hybrid approach.
Keywords
Indoor Positioning; Fingerprinting; K-NN; MinMax; Trilateration; GPS;
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Times Cited By KSCI : 1  (Citation Analysis)
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