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- W1913477218 abstract "Filters are derived for estimating the state of a linear dynamic system based on uncertain observations, which suffer from two types of uncertainties simultaneously. The first uncertainty is a stochastic process with given distribution. The second uncertainty is only known to be bounded, the exact underlying distribution is unknown. The new estimators combine set theoretic and stochastic estimation in a rigorous manner and provide a continuous transition between the two classical estimation concepts. They converge to a set theoretic estimator, when the stochastic error goes to zero, and to a Kalman filter, when the bounded error vanishes. In the mixed noise case, solution sets are provided that are uncertain in a stochastic sense." @default.
- W1913477218 created "2016-06-24" @default.
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- W1913477218 date "2003-01-22" @default.
- W1913477218 modified "2023-09-27" @default.
- W1913477218 title "New estimators for mixed stochastic and set theoretic uncertainty models: the scalar measurement case" @default.
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- W1913477218 doi "https://doi.org/10.1109/cdc.1999.830919" @default.
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