The marginalized particle filter is a powerful combination of the particle filter and the Kalman filter, which can be used when the underlying model contains a linear sub-structure, subject to Gaussian noise. This paper will illustrate several positioning and target tracking applications, solved using the marginalized particle filter. Furthermore, we analyze several properties of practical importance, such as its computational complexity and how to cope with quantization effects.
The marginalized particle filter in practice
2006 IEEE Aerospace Conference ; 11 pp.
2006-01-01
4173037 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Tire Radii Estimation Using a Marginalized Particle Filter
Online Contents | 2014
|The Marginalized Particle Filter for Automotive Tracking Applications
British Library Conference Proceedings | 2005
|