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.

Abstract

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.

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