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On combining statistical and set-theoretic estimation
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DOI：10.1016/S0005-1098(99)00011-4.png)
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Abstract
En 中文
We consider 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. The new estimator 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. (C) 1999 Elsevier Science Ltd. All rights reserved.
Keywords:
estimation theory
filtering techniques
measurement noise
mixed noise models
bounded noise
stochastic noise
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