Stochastic Models, Estimation, and Control

In this volume, Chapters 8-10 extend these ideas to consider optimal smoothing in addition to filtering, compensation of linear model inadequacies while exploiting the basic insights of linear filtering (including an initial study of the important extended Kalman filter algorithm), and adaptive estimation based upon linear models in which uncertain parameters are embedded. Subsequently, Chapter 11 properly develops nonlinear stochastic system models, which then form the basis for the design of practical nonlinear estimation algorithms in Chapter 12.