A Probabilistic Perspective on Gaussian Filtering and Smoothing

نویسندگان

  • Marc Peter Deisenroth
  • Henrik Ohlsson
چکیده

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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عنوان ژورنال:
  • CoRR

دوره abs/1006.2165  شماره 

صفحات  -

تاریخ انتشار 2010