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Multimodal Kalman Filtering

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Abstract

A difficult aspect of multimodal estimation is the possible discrepancybetween the sampling rates and/or the noise levels of theconsidered data. Many algorithms cope with these dissimilaritiesempirically. In this paper, we propose a conceptual analysis ofmultimodality where we try to find the “optimal” way of combiningmodalities. More specifically, we consider a simple Kalman filteringframework where several noisy sensors with different samplingfrequences and noise variances regularly observe a hidden state.We experimentally underline some relationships between the samplinggrids and the asymptotic variance of the maximum a posteriori(MAP) estimator. However, the explicit study of the asymptoticvariance seems intractable even in the simplest cases. We describe apromising idea to circumvent this difficulty: exploiting a stochasticmeasurement model for which one can more easily study the averageasymptotic behavior.
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Dates and versions

hal-01208195 , version 1 (02-10-2015)
hal-01208195 , version 2 (22-01-2016)

Identifiers

  • HAL Id : hal-01208195 , version 2

Cite

Anthony Bourrier, Pierre-Olivier Amblard, Olivier J.J. Michel, Christian Jutten. Multimodal Kalman Filtering. ICASSP 2016 - 41st IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2016, Shanghai, China. ⟨hal-01208195v2⟩
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