It is also shown that the designed estimators are optimal, in the sense that they give minimal worst-case estimation error, on the basis of the available finite number of noise-corrupted data, with respect to an ideal MHE filter (obtained by assuming exact knowledge of the system dynamics and of the global solution of the related nonlinear program).
If the bound is tight enough, it provides a good measure in evaluating the worst-case estimation error).
In the case of full observability, an almost optimal filter is derived, where optimality refers to minimizing a worst-case estimation error.
A method is proposed for designing almost-optimal linear filters with finite impulse response, whose worst-case estimation error is at most twice the lowest achievable one.
In this paper, an approach for the direct design of optimal filters is proposed, where optimality refers to the minimization of a suitable worst-case estimation error.
For unknown linewidths in the range [Γmin, Γmax] the worst-case estimation error is minimized by using settings designed for Γmax.
Our goal is to design a robust finite impulse response (FIR) equalizer which guarantees certain bound on the worst-case estimation error.
The algorithms considered include a Kalman filter that minimizes the estimation error variance, an H∞ filter that minimizes the worst-case estimation error, and a robust mixed Kalman/H∞ filter that allows for uncertainties in both the system and measurement matrices.
The error in the estimation of heritability via the two-step procedure in FASTA had only a very small effect on these differences and was noticeable only for low heritabilities, in which case estimation errors for heritability were higher.
Note that in both cases the estimation error is significantly smaller than the estimation error of the initial estimator, which is 0.9%.
