How are state estimators and observers used in process control?

State estimators reconstruct unmeasured states from available measurements for control and monitoring. Kalman filter provides optimal estimation for linear systems with known noise statistics. Extended Kalman Filter (EKF) handles nonlinear systems. Moving Horizon Estimation (MHE) handles constraints. Applications: fault detection (estimate vs measurement deviation), soft sensors (estimate quality variables), unmeasured disturbance estimation, and sensor validation. Implementation: develop state-space process model, tune estimator gains (noise covariance matrices), validate estimation accuracy, and integrate with control strategy. Consider computational requirements for real-time implementation.