Statistics – Applications
Scientific paper
Jan 2008
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008spie.6937e.128k&link_type=abstract
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2007. Edited by Romaniuk, Rys
Statistics
Applications
Scientific paper
In navigation systems with nonlinear filtration algorithms extended Kalman filter is being used to estimate position. In this filter, the state model distribution and all relevant noise destinies are approximated by Gaussian random variable. What is more, this approach can lead to poor precision of estimation. Unscented Kalman filter UKF approximates probability distribution instead of approximating nonlinear process. The state distribution is represented by a Gaussian random variable specified using weighted sigma points, which completely capture true mean and covariance of the distribution. Another solution for the general filtering problem is to use sequential Monte Carlo methods. It is particle filtering PF based on sequential importance sampling where the samples (particles) and their weights are drawn from the posterior distribution.
Konatowski Stanisław
Pudlak Barbara
No associations
LandOfFree
Influence of a non-Gaussian state model on the position estimation in the nonlinear filtration does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Influence of a non-Gaussian state model on the position estimation in the nonlinear filtration, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Influence of a non-Gaussian state model on the position estimation in the nonlinear filtration will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-1258470