Mixture truncated unscented Kalman filtering
MetadataShow full item record
This paper proposes a computationally efficient nonlinear filter that approximates the posterior probability density function (PDF) as a Gaussian mixture. The novelty of this filter lies in the update step. If the likelihood has a bounded support made up of different regions, we can use a modified prior PDF, which is a mixture, that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to each component of the modified prior mixture can improve the approximation to the posterior provided by the Kalman filter. In practice, bounded support is not necessary. © 2012 ISIF (Intl Society of Information Fusi).
Showing items related by title, author, creator and subject.
Beard, Michael; Vo, Ba Tuong; Vo, Ba-Ngu; Arulampalam, S. (2012)The standard Gaussian Mixture Probability Hypothesis Density (GMPHD) filter and Cardinalised Probability Hypothesis Density (GMCPHD) filter require the target birth model to take the form of a Gaussian mixture. Although ...
Mahler, Ronald (2016)© 2016 ISIF. The generalized labeled multi-Bernoulli (GLMB) filter, introduced by B.-T. Vo and B.-N. Vo in 2013, is an exact closed-form solution of the multitarget recursive Bayes filter, based on the theory of labeled ...
Mahler, Ronald (2014)© 2014 IEEE. In previous publications the author introduced CPHD filters designed to detect and track multiple targets in unknown, dynamically changing clutter backgrounds. The first such filters employed Poisson clutter ...