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 a case 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. In order to reduce complexity and make the tuning easier, a decomposition of the nonlinear process model into two simpler subsystems is proposed. A different type of observer is considered for each subsystem—a particle filter and an unscented Kalman filter. The performance is evaluated for simulated and real-world data and compared with the centralized setting for 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.