UC San Diego

Adaptive observation seeks to move sensor vehicles in order to accurately estimate and forecast the state of a system. This thesis seeks to formulate an adaptive observation algorithm around the Ensemble Kalman Filter. The Monte Carlo approach of the Ensemble Kalman filter allows for the approximation of the variance of the system estimate, which can be used to move the vehicles in a manner that minimizes this variance. After an introduction to the problem, this thesis gives a brief history of the Ensemble Kalman filter before describing the formulation of the adaptive observation algorithm. It then goes on to describe the numerical simulation setup that is used to perform experiments that test the algorithm’s performance. The results show the success of my adaptive observation algorithm in reducing the variance of the system estimate and therefore the ability of my algorithm to produce an accurate estimate of a model two-dimensional convective flow.