Abstract
The article presents an easy to implement approach for indoor localization and navigation that combines Bayesian filtering with support vector machine classifiers to associate high-dimensionality cellular telephone network received signal strength fingerprints to distinct spatial regions. The technique employs a “space sampling” and a “time sampling” scheme in the training procedure, and the Bayesian filter allows introducing a priori information on room layout and target trajectories, resulting in robust room-level indoor localization.
Highlights
A variety of localization and tracking approaches based on Global Positioning System (GPS) and other satellitebased systems have provided solutions for outdoor environments [1]
5.2 Results of Bayesian filtering Bayesian filtering results on the tracking set are shown in Table 4, where we compare the raw results of Support vector machines (SVMs) classification to the results obtained after Bayesian filtering
Some fingerprints were acquired in the corridor and were not taken into account, since the SVM only classifies the seven rooms
Summary
A variety of localization and tracking approaches based on Global Positioning System (GPS) and other satellitebased systems have provided solutions for outdoor environments [1]. Methods based on the measurement of received signal strength (RSS) in RF networks, such as Wi-Fi and Bluetooth networks, for example, have proven to be effective [2,3,4,5,6,7,8,9] These are low-cost and simple to implement because wireless system receivers commonly possess RSS measurement capabilities. Has poor penetration, and 4G is not deployed yet in most places, GSM was chosen for our indoor localization studies since its ubiquity avoids the need for time- and labor-consuming infrastructure deployment and maintenance. It is shown in [10] that GSM signal strengths have smaller fluctuations in time than 2.4-GHz Wi-Fi signals. Each carrier is labeled with an absolute radio-frequency channel number (ARFCN) as shown in Table 1 [18]
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