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Proceedings Paper

A Gaussian mixture ensemble transform filter for vector observations
Author(s): Santosh Nannuru; Mark Coates; Arnaud Doucet
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Paper Abstract

The ensemble Kalman filter relies on a Gaussian approximation being a reasonably accurate representation of the filtering distribution. Reich recently introduced a Gaussian mixture ensemble transform filter which can address scenarios where the prior can be modeled using a Gaussian mixture. Reichs derivation is suitable for a scalar measurement or a vector of uncorrelated measurements. We extend the derivation to the case of vector observations with arbitrary correlations. We illustrate through numerical simulation that implementation is challenging, because the filter is prone to instability.

Paper Details

Date Published: 23 May 2013
PDF: 10 pages
Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87450G (23 May 2013); doi: 10.1117/12.2016129
Show Author Affiliations
Santosh Nannuru, McGill Univ. (Canada)
Mark Coates, McGill Univ. (Canada)
Arnaud Doucet, Univ. of Oxford (United Kingdom)

Published in SPIE Proceedings Vol. 8745:
Signal Processing, Sensor Fusion, and Target Recognition XXII
Ivan Kadar, Editor(s)

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