A General Perspective on Gaussian Filtering and Smoothing: Explaining Current and Deriving New Algorithms
Marc P Deisenroth and Henrik Ohlsson
In: 2011 American Control Conference, June 29 - July 1, 2011, San Francisco, CA, USA.
We present a general probabilistic perspective on
Gaussian filtering and smoothing. This allows us to show that
common approaches to Gaussian filtering/smoothing can be dis-
tinguished solely by their methods of computing/approximating
the means and covariances of joint probabilities. This implies
that novel filters and smoothers can be derived straightfor-
wardly by providing methods for computing these moments.
Based on this insight, we derive the cubature Kalman smoother
and propose a novel robust filtering and smoothing algorithm
based on Gibbs sampling.
|EPrint Type:||Conference or Workshop Item (Paper)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Marc Deisenroth|
|Deposited On:||19 October 2011|