[Back]

M. Thienelt, A. Eichhorn, A. Reiterer:
"Intelligent Pedestrian Positioning in Vienna: Knowledge-Based Kalman-Filtering (wikaf)";
in: "ISPRS Band XXXVI, Teil 5/C55", N. El-Sheimy, A. Vettore (ed.); issued by: CIRGEO, Uni Padua; ISPRS Buchreihe, Padua, Italien, 2008, (invited), 315 - 321.

In this paper the prototype of a map-independent knowledge-based Kalman filter (`WiKaF´) for optimal pedestrian positioning is presented. The WiKaF concept, its system architecture and the integrated sensors are described. The multi-sensor system comes from the NAVIO project (another project for pedestrian navigation in Vienna) and contains a Dead Reckoning Module DRM III, a barometer PTB 220, a digital compass HMR 3000 and an eTrex Summit GPS receiver. The two main components of the position module are introduced. At the present time the knowledge-based component is responsible for the pre-filtering process of the measurement data which includes a first step of outlier detection. In a further step the central Kalman filter derives the optimal position of the pedestrian. For support in dead reckoning scenarios the filters system equations connect the multi-sensor output with a causal motion model. The combination of knowledge-based component and Kalman filtering preliminary aims at an increasing reliability of the filter. At the end of the paper test results in outdoor and indoor scenarios are presented. It is obvious that in many parts WiKAF works track stable but also requires further improvement in extensive dead reckoning scenarios.