Kullback–Leibler divergence approach to partitioned update Kalman filter
MetadataShow full item record
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 applied first. In this paper, we generalize partitioned update Kalman filter, which requires the use of the second order extended Kalman filter, so that it can be used with any Kalman filter extension such as the unscented Kalman filter. To do so, we use a Kullback–Leibler divergence approach to measure the nonlinearity of the measurement, which is theoretically more sound than the nonlinearity measure used in the original partitioned update Kalman filter. Results show that the use of the proposed partitioned update filter improves the estimation accuracy.
Showing items related by title, author, creator and subject.
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 ...
Garcia Fernandez, Angel; Morelande, M.; Grajal, J. (2012)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 ...
A method for processing GNSS data from regional reference networks to enable single-frequency PPP-RTKZhang, B.; Odijk, Dennis (2015)Global Navigation Satellite System (GNSS) data from reference station networks deployed globally can facilitate positioning, navigation and timing applications. To enable precise positioning for dual-frequency users, ...