Non-Linear Filtering based on Observations from Gaussian Processes
2011 (English)In: Proceedings of the 2011 IEEE Aerospace Conference, 2011, , 6 p.Conference paper (Refereed)
We consider a class of non-linear filtering problems, where the observation model is given by a Gaussian process rather than the common non-linear function of the state and measurement noise. The new observation model can be considered as a generalization of the standard one with correlated measurement noise in both time and space. We propose a particle filter based approach with a measurement update step that requires a memory of past observations which can be truncated using a moving window to obtain a finite-dimensional filter with arbitrarily good accuracy. The validity of the conceptual solution is proved via simulations on a one dimensional tracking problem and implementation issues are discussed.
Place, publisher, year, edition, pages
2011. , 6 p.
Gaussian processes, Nonlinear filters, Particle filtering (numerical methods)
IdentifiersURN: urn:nbn:se:liu:diva-76075DOI: 10.1109/AERO.2011.5747440ISBN: 978-1-4244-7350-2OAI: oai:DiVA.org:liu-76075DiVA: diva2:512104
2011 IEEE Aerospace Conference, Big Sky, MT, USA, 5-12 March, 2011