A data-driven moving horizon estimator is proposed for linear systems with unknown parameters, proving that its expected estimation error is ultimately bounded and relating the bound to noise covariances and offline data length.
Remote estimation for energy harvesting systems under multiplicative noises: A binary encoding scheme with probabilistic bit flips
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Data-Driven Moving Horizon Estimators for Linear Systems with Sample Complexity Analysis
A data-driven moving horizon estimator is proposed for linear systems with unknown parameters, proving that its expected estimation error is ultimately bounded and relating the bound to noise covariances and offline data length.