Glopos Fzc

Timo Heikkila

Mikael Vainio

A method for locating an electronic mobile device capable of wireless communication, the method including obtaining a probability map structure for the area of interest, the probability map structure associating environment data values, preferably including cellular network based data, with location data, obtaining a number of probabilistic temporospatial motion models for different types of motion, the models at least partly defining transition probabilities between location estimates relative to time, obtaining at least environment data captured or determined by the mobile device at a plurality of sequential time instances, and determining the most probable location estimate of the mobile device based on combining probability data from both the probability map structure and the motion models, wherein the at least environment data is best fitted in the motion models and probability map structure according to predefined criteria. Related two arrangements and a computer program are presented.

Method and arrangement for locating a mobile device

Electronic arrangement for positioning a mobile device, including a mapping entity configured to obtain positioning data, from a plurality of measuring mobile devices present in an area of interest, establish and maintain, based on the obtained data, a multi-level probability map model structure for the area, wherein each higher level covers the area with lower spatial resolution by a plurality of determine sub-areas, each having a unitary character, and each lower level correspondingly covers, for each the sub-area of the adjacent upper level, a plurality of determined sub-areas thereof with higher spatial resolution, the lowest level determining the highest spatial resolution location elements of the model, optionally coordinates, a locating entity configured to obtain data provided by the mobile device, determine an estimate of the position of the mobile device by traversing through at least a portion of a number of vertical candidate paths of the multi-level probability model structure.

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Hyvinkaa, Finland

Timo Heikkila