Abstract
This paper investigates the filter design problem for linear time-invariant dynamic systems when no mathematical model is available, but a set of initial experiments can be performed where also the variable to be estimated is measured. Two-step and direct approaches are considered within both a stochastic and a deterministic framework and optimal or suboptimal solutions are reviewed.
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Milanese, M., Ruiz, F., Taragna, M. (2013). From Model-Based to Data-Driven Filter Design. In: d'Onofrio, A. (eds) Bounded Noises in Physics, Biology, and Engineering. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser, New York, NY. https://doi.org/10.1007/978-1-4614-7385-5_16
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