Abstract
Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver’s location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver’s motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios.
Highlights
The global positioning system (GPS) permits solving the positioning problem in a reliable manner
These results show that our proposed method significantly outperforms the fine-grained indoor localization (FILA), DeepFi and PhaseFi methods in both scenarios
We proposed a new fingerprint-based method for indoor localization based on channel state information (CSI)
Summary
The global positioning system (GPS) permits solving the positioning problem in a reliable manner. This approach is limited to outdoor environments, since GPS signals do not reach indoor receivers. A number of techniques are available for indoor positioning systems [1,2]. These techniques include the use of ultra-wideband signals [3,4,5], Wi-Fi signals [6,7,8], bluetooth signals [9,10], radio-frequency identification [11,12,13], odometry measurements [14], etc. Obtaining a high-precision and reliable indoor positioning method has become the main research problem in various location-based services
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