A Probabilistic Perspective on Gaussian Filtering and Smoothing
File(s)
Working paper
Author(s)
Deisenroth, MP
Ohlsson, H
Type
Report
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 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.
Date Issued
2010-12-31
Copyright Statement
© 2011 The Authors
Description
15.07.13 KB. Ok to add report to Spiral.
Identifier
http://arxiv.org/abs/1006.2165v5
Notes
14 pages. Extended version of conference paper (ACC 2011)