Title :
A new algorithm for state estimation of stochastic linear discrete systems
Author_Institution :
Dept. of Syst. Eng., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia
fDate :
8/1/1994 12:00:00 AM
Abstract :
A novel algorithm is proposed for state estimation of linear discrete-time systems. The procedure performs explicit minimization of the innovation variance and is based upon the principle of pseudo linear regression (PLR) method. Sufficient conditions for algorithm convergence are also derived
Keywords :
discrete time systems; estimation theory; filtering and prediction theory; minimisation; state estimation; statistical analysis; stochastic systems; algorithm convergence; innovation variance minimization; linear discrete-time systems; pseudo linear regression; state estimation; stochastic linear discrete systems; Convergence; Covariance matrix; Kalman filters; Linear regression; Minimization methods; Noise measurement; Riccati equations; State estimation; Stochastic systems; Technological innovation;
Journal_Title :
Automatic Control, IEEE Transactions on
