Matti Raitoharju

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Indoor positioning based on WLAN signals is often enhanced using pedestrian dead reckoning (PDR) based on an inertial measurement unit. The state evolution model in PDR is usually nonlinear. We present a new linear state evolution model for PDR. In simulated-data and real-data tests of tightly coupled WLAN-PDR positioning, we find that the positioning(More)
Technology. Hybrid positioning is a process where measurements from different sources are used to obtain position estimate. PNaFF provides tools for comparison and visualization of the performance of hybrid positioning filters that estimate the current state from measurements and the previous state estimate. New filters can be added to PNaFF easily and the(More)
Fingerprint-based (FP) positioning methods determine a receiver's position using a database of radio signal strength measurements that were collected earlier at known locations. For positioning with WLAN signals, nonparametric methods such as the weighted k-nearest neighbour (WKNN) method are widely used. Due to their large data storage and transmission(More)
In the Gaussian mixture approach a Bayesian posterior probability distribution function is approximated using a weighted sum of Gaussians. This work presents a novel method for generating a Gaussian mixture by splitting the prior taking the direction of maximum nonlinearity into account. The proposed method is computationally feasible and does not require(More)
Kalman filter extensions are commonly used algorithms for nonlinear state estimation in time series. In the literature, different approaches have been proposed to exploit the structure of the state and measurement models to reduce the computational demand of the algorithms. We present these code optimizations in a general form that allows them to be used(More)
—We present an empirical model for noises in color measurements from OLED displays. According to measured data the noise is not isotropic in the XYZ space, instead most of the noise is along an axis that is parallel to a vector from origin to measured XYZ vector. The presented empirical model is simple and depends only on the measured XYZ values. Our tests(More)
For nonlinear systems there exist several Kalman filter extensions that linearize or do moment matching to approximate the nonlinear update. These algorithms usually assume Gaussian measurement noises. The assumption of Gaussian noises degrades the performance when the data contain outliers or are otherwise non-Gaussian. In this paper, we present a new way(More)