An "extended" version of this method that can be applied to nonlinear systems linearizes about the current mean and covariance. The namesake "gain" or "blending factor" used in this method is a 3 matrix that minimizes the a posteriori error covariance. This method is combined with the linear quadratic regulator to solve the linear quadratic Gaussian control problem. This method is used for systems with a state vector equal to the state transition matrix times the state vector plus the control 6 matrix times the control vector plus the process noise vector, and an observation vector equal tothe observation matrix times the state vector plus the measurement noise vector. It has two phases,predict and update, and it grew out of a paper in whic
An "extended" version of this method that can be applied to nonlinear systems linearizes about the current mean and covariance. The namesake "gain" or "blending factor" used in this method is a 3 matrix that minimizes the a posteriori error covariance. This method is combined with the linear quadratic regulator to solve the linear quadratic Gaussian control problem. This method is used for systems with a state vector equal to the state transition matrix times the state vector plus the control 6 matrix times the control vector plus the process noise vector, and an observation vector equal tothe observation matrix times the state vector plus the measurement noise vector. It has two phases,predict and update, and it grew out of a paper in which a recursive solution to the discrete-data linear 9 filtering problem was found. For 10 points, name this purely time-domain (*) filter that provides anoptimal estimate for many linear stochastic systems.
An "extended" version of this method that can be applied to nonlinear systems linearizes about the current mean and covariance.
Kalman filter [prompt on: linear quadratic estimator, LQE]