State estimation with partially observed inputs: a unified Kalman filtering approach
For linear stochastic time-varying state space models with Gaussian noises, this paper investigates state estimation for the scenario where the input variables of the state equation are not fully observed but rather the input data is available only at an aggregate level. Unlike the existing filters...
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| Format: | Default Article |
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2013
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| Online Access: | https://hdl.handle.net/2134/12188 |
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