Recursive State Estimation of Nonlinear Systems with Applications to Integrated Navigation
2000 (English)Report (Other academic)
Today, accurate navigation systems often consists of several separate navigation systems which are integrated together to provide a more accurate and reliable navigation solution. Integrated navigation is a nonlinear and non-Gaussian system integration problem. Sequential Monte Carlo methods (particle filters) handle recursive state estimation of arbitrary systems. A direct application of the particle filter shows that a vast number of particles is needed for the filter to work well. Another method is developed in this report using the typical structure of an integrated navigation system. This method takes advantage of the parts of the state equations that are linear and Gaussian and estimates these parts using the Kalman filter. The nonlinear and non-Gaussian parts are estimated using the particle filter. Based on Bayes rule the solutions from the two filters are fused together. This two-filter approach is applied to the same system and the result shows that the number of particles needed to achieve the same performance as the particle filter is much less.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2000. , 22 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2321
Particle filter, Kalman filter, State estimation, Nonlinear navigation
IdentifiersURN: urn:nbn:se:liu:diva-55767ISRN: LiTH-ISY-R-2321OAI: oai:DiVA.org:liu-55767DiVA: diva2:316565