A Gaussian mixture ensemble transform filter for vector observations

Santosh Nannuru; Mark Coates; Arnaud Doucet

doi:10.1117/12.2016129

23 May 2013 A Gaussian mixture ensemble transform filter for vector observations

Santosh Nannuru, Mark Coates, Arnaud Doucet

Proceedings Volume 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII; 87450G (2013) https://doi.org/10.1117/12.2016129
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

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.

Citation Download Citation

Santosh Nannuru, Mark Coates, and Arnaud Doucet "A Gaussian mixture ensemble transform filter for vector observations", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87450G (23 May 2013); https://doi.org/10.1117/12.2016129

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