Moving Horizon Estimator with filtering and adaptive sampling

Optimisation based algorithms known as Moving Horizon Estimator (MHE) have been developed through the years. In this work, we propose two solutions to decrease the computational cost of MHE, limiting its applicability in real-time applications. The proposed solutions rely on output filtering and adaptive sampling. The use of filters reduces the total amount of data by shortening the length of the moving window (buffer) and consequently decreasing the time consumption for plant dynamics integration. The proposed adaptive sampling policy allows for discarding data that do not yield significant improvements in the estimation error. Simulations on several cases are provided to corroborate the effectiveness of the proposed strategies.