Sherman’s theorem

I present the use of Sherman’s Theorem, and a development approach, for optimal solutions to real-time estimation problems that are multidimensional, nonlinear, 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. Sherman’s Theorem is not new, but my application of Sherman’s Theorem is new.To Malcolm Shuster, who taught me about torque replacement modeling with rate-gyro sensors, and argues fiercely in defense of Maximum Likelihood Estimation (MLE).