On combining set theoretic and Bayesian estimation

23 December 2002

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

Vol. 4 (10504729) , 3081-3086
https://doi.org/10.1109/robot.1996.509180

Abstract

Considers state estimation based on observations which are simultaneously corrupted by a deterministic amplitude-bounded unknown bias and a possibly unbounded random process. This problem is solved by developing a combined set theoretic and Bayesian recursive estimator. It provides a continuous transition between both concepts in that it converges to a set theoretic estimator when the stochastic error vanishes and to a Bayesian estimator when the deterministic error vanishes. In the mixed noise case, the new estimator supplies solution sets defined by bounds that are uncertain in a statistical sense.

Keywords

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