Kalman filter based estimation of flow states in open channels using Lagrangian sensing

In this article, we investigate real-time estimation of flow states, average velocity and stage (water depth), in open channels using the measurements obtained from Lagrangian sensors (drifters). One-dimensional Shallow Water Equations (SWE), also known as Saint-Venant equations, are used as the mathematical model for the flow. After linearizing and discretizing the PDEs using an explicit linear scheme, we construct a linear state-space model of the flow. The Kalman filter is then used to estimate the states by incorporating the measurements obtained from passive drifters. Drifters which are equipped with GPS recievers move with the flow and report their position at every time step. The position of the drifters at every time step are used to approximate the average velocity of the flow at the corresponding locations and time step. The method is implemented in simulation on a section of the Sacramento river in California using real data and the results are validated with a two-dimension...

• Type: Conference
• Year: 2009
• Where: CDC
• Authors: Mohammad Rafiee, Qingfang Wu, Alexandre M. Bayen
• Keywords: Average Velocity, CDC 2009, Control Systems, Lagrangian Sensors, Time Step