Linear Estimation: The Kalman-Bucy Filter
William Douglas Schindel · Rose-Hulman Scholar (Rose–Hulman Institute of Technology) · 1972
The problem of linear dynamic estimation, its solution as developed by Kalman and Bucy, and interpretations, properties and illustrations of that solution are discussed. The central problem considered is the estimation of the system state vector X, describing a linear dynamic system governed by dx/dt = F(t)X(t) + G(t)U(t) Y(t) = H(t)X(t) + V(t) for observations of Y (system output), where V is a random observation-corrupting process, and U is a random system driving process. An extension of the Kalman-Bucy filter to estimation in the absence of priori knowledge of the random process U and V is developed and illustrated.