Truncated unscented kalman filtering
MetadataShow full item record
We devise a filtering algorithm to approximate the first two moments of the posterior probability density function (PDF). The novelties of the algorithm are in the update step. If the likelihood has a bounded support, we can use a modified prior distribution that meets Bayes' rule exactly. Applying a Kalman filter (KF) to the modified prior distribution, referred to as truncated Kalman filter (TKF), can vastly improve the performance of the conventional Kalman filter, particularly when the measurements are informative relative to the prior. The application of the TKF to practical problems in which the measurement noise PDF has unbounded support is achieved by imposing several approximating assumptions which are valid only when the measurements are informative. This implies that we adaptively choose between an approximation to the KF or the TKF according to the information provided by the measurement. The resulting algorithm based on the unscented transformation is referred to as truncated unscented KF.
Showing items related by title, author, creator and subject.
Raitoharju, M.; Garcia Fernandez, Angel; Piché, R. (2017)© 2016 Elsevier B.V. Kalman filtering is a widely used framework for Bayesian estimation. The partitioned update Kalman filter applies a Kalman filter update in parts so that the most linear parts of measurements are ...
El-Mowafy, Ahmed; Mohamed, A. (2005)An adaptive Kalman filtering approach is proposed for attitude determination to replace the fixed (conventional) Kalman filtering approach. The filter is used to adaptively reflect system dynamics changes or rapid changes ...
Mahler, Ronald (2011)Bayes' rule and Dempster's combination are typically presumed to be radically different procedures for fusing evidence. This paper demonstrates that measurement-update using Dempster's combination is a special case of ...