After studying this chapter you will be able to
• use an observer to estimate the state vector of a linear time-invariant system;
• use a Kalman filter to estimate the state vector of a linear system using knowledge of the system matrices, the system input and output measurements, and the covariance matrices of the disturbances in these measurements;
• describe the difference among the predicted, filtered, and smoothed state estimates;
• formulate the Kalman-filter problem as a stochastic and a weighted least-squares problem;
• solve the stochastic least-squares problem by application of the completion-of-squares argument; ...