A Probabilistic Perspective on Gaussian Filtering and Smoothing

Details A Probabilistic Perspective on Gaussian Filtering and Smoothing A Probabilistic Perspective on Gaussian Filtering and Smoothing

2010-06-10

arxiv.org/abs/1006.2165v5

Statistics

Methodology

14 pages. Extended version of conference paper (ACC 2011)

Scientific paper

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 distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This implies that novel filters and smoothers can be derived straightforwardly 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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