Abstract

Model Based Smoothing of Linear and Nonlinear Processes

Arthur Krener
University of California at Davis

We introduce the problems of filtering and smoothing of data based on linear and nonlinear models. There are several approaches both
deterministic and stochastic including the Kalman filter, the least squares filter and the min-max filter. We discuss the extension of these to the smoothing problem and the complications due to nonlinearities and/or boundary data.