Ekahau Oy

Bayes Information Technology, Ltd.

Kimmo Valtonen

Petri Jukka Myllymäki

Henry Tirri

Petri Kontkanen

Antti Tuominen

Hannes Wettig

Jussi Lahtinen

Teemu Roos

Jussi Mika Antero Lahtinen

Tomi Silander

Tomi Viljam Silander

A method for estimating a receiver's location in a wireless communication environment having several channels. Each channel has at least one signal parameter that varies with location differently from the other channels. A set of calibration data is determined for each calibration point, each set including the location and at least one measured signal parameter for each of several channels. The calibration data serve as a basis for a statistical model of the signal parameters versus a receiver's location. A set of observed signal parameters is determined, the set including at least one signal parameter for each of several channels at the receiver's location. A location estimate approximating the location of the receiver is determined on the basis of the statistical model and the set of observed signal parameters.

Location estimation in wireless telecommunication networks

A method for determining an error estimate concerning a target device's location. The target device moves and communicates in a wireless environment using signals having at least one measurable signal value. A probabilistic model of the wireless environment indicates a probability distribution for signal values at several sample points in the wireless environment. A set of observations of signal values is made and the target device's location is estimated based on the probabilistic model and the set of observations. The error estimate is determined as a combination of products over several sample points. Each product comprises a probability distribution for the sample point in question being the target device's location and a distance function between the sample point in question and the target device's estimated location.

Error estimate concerning a target device's location operable to move in a wireless environment

A method and system for transforming a multivariate data domain into a low-dimensional visual representation. Probabilistic models of the data domain are utilized, and at least one probabilistic model is used to produce at least one predictive distribution. The predictive distributions are used as inputs to the visualization process, where the multidimensional space is converted to a low-dimensional space. In this process data vectors are considered similar, for example, if the corresponding instances of a predictive distribution, conditioned with the variable value assignments found in the data vectors, are similar. Consequently, similarity is not defined directly using the physical properties of the data vectors, but indirectly through the probabilistic predictive model(s).

Visualization method and visualization system

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Helsinki, Finland

Kimmo Valtonen