Stanislav Zonov | Applied Math, University of Waterloo
Burgers Sensor Placement
The algorithm used for determining sensor placement in this thesis will be based on the Kalman filter. This filter is very famous and its application are numerous - some examples include aircraft navigation, finance and weather forecasting. It is used to extract an estimate of the true state of a system based on noisy measurements. For linear systems, where the noise satisfies certain assumptions, the Kalman filter minimizes the expected squared error between the filter's estimate and the true state of the system. The error based minimization property with the combination of varying sensor position allows for an intuitive brute force type algorithm that by varying the sensor location further minimizes the expected squared error to produce an optimal sensor placement. This thesis examines this concept further, for nonlinear systems.