Decentralized Estimation of Overflow Losses in a Hopper-Dredger

The Kalman filter and its nonlinear variants have been widely used for filtering and state estimation. However, models with severe nonlinearities are not handled well by Kalman filters. Such an application is presented in this paper: the estimation of the overflow losses in a hopper-dredger. The overflow mixture density and flow-rate have to be estimated based on noisy measurements of the total hopper volume, mass, incoming mixture density and flow-rate. We propose a decomposition of the nonlinear process model into two simpler subsystems. Different types of observers are considered for each sub-process. The performance is evaluated for simulated and real-world data and compared for the centralized and distributed settings and four combinations of the particle filter and the unscented Kalman filter. The results indicate that the distributed observer achieves the same performance as the centralized one, while leading to increased modularity, reduced complexity, lower computational costs and easier tuning.