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Urban Forssell
Researcher at Nira Dynamics AB
Publications - 37
Citations - 3461
Urban Forssell is an academic researcher from Nira Dynamics AB. The author has contributed to research in topics: Identification (information) & System identification. The author has an hindex of 20, co-authored 37 publications receiving 3291 citations. Previous affiliations of Urban Forssell include Mecel & Linköping University.
Papers
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Journal ArticleDOI
Particle filters for positioning, navigation, and tracking
Fredrik Gustafsson,Fredrik Gunnarsson,Niclas Bergman,Urban Forssell,Jonas Jansson,Rickard Karlsson,Per-Johan Nordlund +6 more
TL;DR: The technique of map matching is used to match an aircraft's elevation profile to a digital elevation map and a car's horizontal driven path to a street map and it is shown that the accuracy is comparable with satellite navigation but with higher integrity.
Journal ArticleDOI
Closed-loop identification revisited
Urban Forssell,Lennart Ljung +1 more
TL;DR: A new projection approach to closed-loop identification with the advantage of allowing approximation of the open-loop dynamics in a given, and user-chosen frequency domain norm, even in the case of an unknown, nonlinear regulator.
Journal ArticleDOI
Brief Some results on optimal experiment design
Urban Forssell,Lennart Ljung +1 more
TL;DR: The problem of designing the identification experiments to make them maximally informative with respect to the intended model use is studied.
Closed-loop Identification : Methods, Theory, and Applications
TL;DR: System identification deals with constructing mathematical models of dynamical systems from measured data, which have important applications in many technical and nontechnical areas, such as medicine and engineering.
Patent
Adaptive filter model for motor vehicle sensor signals
TL;DR: In this paper, a sensor system for combining first and second sensor signals, and generating a physical parameter values dependent on said sensor signals used in autocalibrating sensors improving the performance and quality of existing sensor signals and virtual sensors realising new sensors by combining and integrating in adaptive filter models sensor signals representing same or different types of physical parameters.