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In practical applications, state estimation requires the consideration of stochastic and systematic errors. If both error types are present, an exact probabilistic description of the state estimate is not possible, so that common Bayesian estimators have to be questioned. This paper introduces a theoretical concept, which allows for incorporating unknown but bounded errors into a Bayesian inference...
A novel distributed sensor scheduling method for large-scale sensor networks observing space-time continuous physical phenomena is introduced. In a first step, the model of the distributed phenomenon is spatially and temporally decomposed leading to a linear probabilistic finite-dimensional model. Based on this representation, the information gain of sensor measurements is evaluated by means of the...
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