Hybrid Spatial Interpolation : RSS based Indoor localization
Haq, Zuhair Ul (2021)
Haq, Zuhair Ul
2021
Tietotekniikan DI-ohjelma - Master's Programme in Information Technology
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2021-11-25
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202110318033
https://urn.fi/URN:NBN:fi:tuni-202110318033
Tiivistelmä
GNSS is a constellation of satellites that provides global positioning, navigation, and tracking in outdoor spaces. However, due to complex infrastructure, the satellite signals become weak in the indoor environment, and therefore, GNSS cannot provide reliable positioning. The indoor environment comes packed with radio signals generated by WIFI and Bluetooth access points. The RSS of the radio signals in indoor spaces can be used to provide accurate indoor positioning. Furthermore, radio access points deployment is increasing steadily in indoor spaces, which makes it ideal for indoor positioning.
RSS-based indoor localization is a two-step process, the frst step being RSS fngerprinting, where RSS measurements are recorded along with reference location coordinates to generate radio maps. The second step is the positioning step, where real-time RSS measurements are collected and compared with radio maps to estimate the user’s location. However, fngerprinting is an arduous task that requires time and workforce. This leads to the need for methods that can generate radio maps from little recorded radio measurements.
The goal of the thesis was to analyze various interpolation and extrapolation methods in tradi tional RSS fngerprinting and investigate their effects on overall indoor positioning. The advantage of these extrapolation and interpolation methods is to reduce the overhead of collecting data and covering those areas which are not accessible to users. In addition, these methods can also help automate the process of fngerprinting, leading to a much wider deployment of indoor positioning services at a lower cost. The thesis evaluates three different interpolation and extrapolation meth ods based on fve evaluation parameters: mean error, maximum error, building detection, floor detection, and consistency of indoor positioning.
For evaluation purposes, actual RSS measurements were recorded using smartphones in an indoor environment. The experimental building was a multistory offce space consisting of com plex indoor infrastructure. The test RSS measurements were classifed into edge and non-edge measurements and studied separately. Out of three methods compared, a hybrid method that combines Delaunay triangulation and RSS-based spatial interpolation performed the best.
The hybrid method harnesses the advantages of two interpolation and extrapolation method ologies; Delaunay triangulation with linear interpolation and spatial interpolation. The use of De launay triangulation makes the process simpler with very little computational complexity. The RSS-based spatial interpolation uses a physical radio path loss model that makes it feasible for deployment in diverse indoor environments.
RSS-based indoor localization is a two-step process, the frst step being RSS fngerprinting, where RSS measurements are recorded along with reference location coordinates to generate radio maps. The second step is the positioning step, where real-time RSS measurements are collected and compared with radio maps to estimate the user’s location. However, fngerprinting is an arduous task that requires time and workforce. This leads to the need for methods that can generate radio maps from little recorded radio measurements.
The goal of the thesis was to analyze various interpolation and extrapolation methods in tradi tional RSS fngerprinting and investigate their effects on overall indoor positioning. The advantage of these extrapolation and interpolation methods is to reduce the overhead of collecting data and covering those areas which are not accessible to users. In addition, these methods can also help automate the process of fngerprinting, leading to a much wider deployment of indoor positioning services at a lower cost. The thesis evaluates three different interpolation and extrapolation meth ods based on fve evaluation parameters: mean error, maximum error, building detection, floor detection, and consistency of indoor positioning.
For evaluation purposes, actual RSS measurements were recorded using smartphones in an indoor environment. The experimental building was a multistory offce space consisting of com plex indoor infrastructure. The test RSS measurements were classifed into edge and non-edge measurements and studied separately. Out of three methods compared, a hybrid method that combines Delaunay triangulation and RSS-based spatial interpolation performed the best.
The hybrid method harnesses the advantages of two interpolation and extrapolation method ologies; Delaunay triangulation with linear interpolation and spatial interpolation. The use of De launay triangulation makes the process simpler with very little computational complexity. The RSS-based spatial interpolation uses a physical radio path loss model that makes it feasible for deployment in diverse indoor environments.