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On combining set theoretic and Bayesian estimation

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3 Author(s)
U. D. Hanebeck ; Dept. of Autom. Control Eng., Tech. Univ. Munchen, Germany ; J. Horn ; G. Schmidt

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

Published in:

Robotics and Automation, 1996. Proceedings., 1996 IEEE International Conference on  (Volume:4 )

Date of Conference:

22-28 Apr 1996