The performance of estimation algorithms is highly dependent on the accuracy of models. Model development is generally the most difficult task in designing an estimator. The equations for least squares and Kalman filtering are well documented, but proper selection of system models and parameters requires a good understanding of the system and of various trade-offs in selecting model structure and states. We start by discussing dynamic and measurement models. This provides the reader with a better understanding of the problem when estimation algorithms are presented in later chapters.

Chapter 1 was deliberately vague about the structure of models because the intent was to present concepts. The dynamic (time evolution) model of the true system used in Figure 1.1 was a generic form. The system state vector x(t) was an n-element linear or nonlinear function of various constant parameters p, known time-varying inputs u(τ) defined over the time interval t0τt, random process noise q(τ) also defined over the time interval t0τt, and possibly time, t:

(2.0-1) c02e000001

Function ft was defined as a time integral of the independent variables. The m-element measurement vector y(t) was assumed to be a function of x(t) with additive random noise:


However, these models are too general to be useful for estimation. We now consider the ...

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