Positioning with Multilevel Coverage Area Models Matti Raitoharju, Marzieh Dashti, Simo Ali-Löytty & Robert Piché 2.12.2018

Problem WLAN maps (radiomaps) are databases generated using fingerprints containing the location and access point IDs What kind WLAN map is best for positioning? Matti Raitoharju Using Unlocated Fingerprints in Generation of WLAN Maps for Indoor Positioning 2.12.2018

Requirements Map size does not depend on number of fingerprints Map is tolerant to outliers Positioning accuracy with these maps is as good as conventional fingerprinting Matti Raitoharju Using Unlocated Fingerprints in Generation of WLAN Maps for Indoor Positioning 2.12.2018

Coverage area models Model the coverage area as Student-t or normal distribution R. Piché, “Robust estimation of a reception region from location fingerprints” Prior is used → model may be constructed even from a single FP Student-t is more robust to outliers Matti Raitoharju Using Unlocated Fingerprints in Generation of WLAN Maps for Indoor Positioning 2.12.2018

Multivariate Student-t models Parameters for construction of a Student-t model r – a typical range of an access point ν – degree of freedom, if ν →∞ the distribution is normal τ – strength of prior (σ=τr2) d – dimension (2) T – number of iterations A coverage area is defined using parameters µ - mean of the model Σ – shape matrix P = ν/(ν-2) Σ – covariance matrix *Oma nimi ja esityksen aihe vaihdettava alatunnisteeseen 2.12.2018

*Oma nimi ja esityksen aihe vaihdettava alatunnisteeseen Student-t regression *Oma nimi ja esityksen aihe vaihdettava alatunnisteeseen 2.12.2018

Positioning With vague prior, position estimate is a weighted average of the coverage area centres Matti Raitoharju Using Unlocated Fingerprints in Generation of WLAN Maps for Indoor Positioning 2.12.2018

Multilevel coverage area models CA approach only uses presence/absence of AP signal (i.e. single RSS threshold level) To better use the RSS information: fit several CAs to each AP (multiple RSS threshold levels) Matti Raitoharju Using Unlocated Fingerprints in Generation of WLAN Maps for Indoor Positioning 2.12.2018

Model comparison 50m Outliers does not change the mean much Matti Raitoharju Using Unlocated Fingerprints in Generation of WLAN Maps for Indoor Positioning 2.12.2018

Storage In our outdoor data For 2-level model, 10 numbers have to be stored 2 means 2 covariance matrices In our outdoor data conventional FP method would need 114 numbers for each AP Matti Raitoharju Using Unlocated Fingerprints in Generation of WLAN Maps for Indoor Positioning 2.12.2018

Test setup Different prior and distribution parameters were tested FP1 FP2 AP1 -50 -60 AP2 -55 -65 AP3 -70 AP4 -75 AP5 AP6 Different prior and distribution parameters were tested Different criteria to set the level thresholds RSS-level Matti Raitoharju Using Unlocated Fingerprints in Generation of WLAN Maps for Indoor Positioning 2.12.2018

Test setup Different prior and distribution parameters were tested FP1 FP2 AP1 -50 -60 AP2 -55 -65 AP3 -70 AP4 -75 AP5 AP6 Different prior and distribution parameters were tested Different criteria to set the level thresholds RSS-level n-strongest Matti Raitoharju Using Unlocated Fingerprints in Generation of WLAN Maps for Indoor Positioning 2.12.2018

Test setup Different prior and distribution parameters were tested FP1 FP2 AP1 -50 -60 AP2 -55 -65 AP3 -70 AP4 -75 AP5 AP6 Different prior and distribution parameters were tested Different criteria to set the level thresholds RSS-level n-strongest x%-strongest Matti Raitoharju Using Unlocated Fingerprints in Generation of WLAN Maps for Indoor Positioning 2.12.2018

Indoor results 50m Matti Raitoharju Using Unlocated Fingerprints in Generation of WLAN Maps for Indoor Positioning 2.12.2018

Indoor results Two CA models improved positioning accuracy compared to one Two and three level models had similar positioning accuracy For all threshold criteria there were good parameters The Student-t models with weak and small prior produced the best results Mean error was slightly smaller than conventional fingerprinting Matti Raitoharju Using Unlocated Fingerprints in Generation of WLAN Maps for Indoor Positioning 2.12.2018

*Oma nimi ja esityksen aihe vaihdettava alatunnisteeseen Indoor results *Oma nimi ja esityksen aihe vaihdettava alatunnisteeseen 2.12.2018

Outdoor results Multilevel coverage area models did not improve positioning performance much Optimal prior was stronger and larger than in indoor test Student-t was worse than normal models Matti Raitoharju Using Unlocated Fingerprints in Generation of WLAN Maps for Indoor Positioning 2.12.2018

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*Oma nimi ja esityksen aihe vaihdettava alatunnisteeseen Outdoor results *Oma nimi ja esityksen aihe vaihdettava alatunnisteeseen 2.12.2018

Conclusions and outlook Use of two level coverage areas slightly improved the positioning accuracy indoors Order of RSS values can be used for dividing FPs to weak and strong Positioning with Student-t? Mixture of Gaussians or Students? Matti Raitoharju Using Unlocated Fingerprints in Generation of WLAN Maps for Indoor Positioning 2.12.2018