The Marginalized Particle Filter in Practice
2006 (English)In: Proceedings of the 2006 IEEE Aerospace Conference, 2006Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
Gaussian noise, Adaptive Kalman filters, Computational complexity, Particle filtering (numerical methods), Position control, Quantisation (signal), Target tracking, Linear sub-structure, Marginalized particle filter, Positioning, Quantization effects
National CategoryEngineering and Technology Control Engineering
IdentifiersURN: urn:nbn:se:liu:diva-13922DOI: 10.1109/AERO.2006.1655922ISBN: 9780780395459OAI: oai:DiVA.org:liu-13922DiVA: diva2:22195
2006 IEEE Aerospace Conference, Big Sky, MT, USA, March, 2006