Title :
An optimal reduced-order stochastic observer-estimator
Author_Institution :
Dept. of Electr. Eng., Wright State Univ., Dayton, OH, USA
fDate :
4/1/1992 12:00:00 AM
Abstract :
An optimal reduced-order observer-estimator (filter) is developed which can provide a full-dimensional vector of state estimates for systems where the dimension of the measurement vector is smaller than that of the state vector and none of the measurements are noise free. The reduced-order filter consists of two subfilters each of which provides a subset of the optimal estimate. A two-step L-K transformation is employed to minimize the estimate error variance of each subfilter. The optimal reduced-order filter developed is computationally efficient
Keywords :
estimation theory; filtering and prediction theory; minimisation; state estimation; stochastic processes; error variance; full-dimensional vector; optimal reduced-order stochastic observer-estimator; reduced-order filter; state estimates; state vector; subfilter; two-step L-K transformation; Large-scale systems; Noise measurement; Noise reduction; Nonlinear filters; Satellites; Space stations; State estimation; Stochastic processes; Stochastic resonance; Stochastic systems;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
