Sherman's Theorem: Solutions to multidimensional nonlinear real-time problems

I present the use of Sherman's Theorem, and a development approach, for optimal solutions to real-time estimation problems that are multidimensional, non-linear, stochastic, and have random multidimensional forcing function modeling errors that drive the state. Satisfaction of Sherman's Theorem guarantees that the mean-squared state estimate error on each state estimate component is minimized. Copyright © 2005 Analytical Graphics, Inc.