  Optimisation-based algorithms known as Moving Horizon Estimator (MHE) have
been developed through the years. This paper illustrates the implementation of
the policy introduced in the companion paper submitted to the 18th IFAC
Workshop on Control Applications of Optimization [Oliva and Carnevale, 2022],
in which we propose two techniques to reduce the computational cost of MHEs.
These solutions mainly rely on output filtering and adaptive sampling. The use
of filters reduces the total amount of data used by MHE, shortening the length
of the moving window (buffer) and consequently decreasing the time consumption
for plant dynamics integration. Meanwhile, the proposed adaptive sampling
policy discards those sampled data that do not allow a sensible improvement of
the estimation error. Algorithms and numerical simulations are provided to show
the effectiveness of the proposed strategies.
