Title :
Optimal reduced-order filtering
Author_Institution :
Dept. of Electr. Eng., Wright State Univ., Dayton, OH, USA
Abstract :
An optimal reduced-order filter is developed which can provide a full vector of state estimates for the case where the dimension of the measurement vector is smaller than that of the state vector and no measurements are noise-free. The reduced-order filter consists of an observer type subfilter and a complementary subfilter, each of which provides a subset of the optimal estimate. A two-step L-K transformation is employed to minimize the estimate error covariance of each subfilter. A target tracking problem is studied as an example
Keywords :
filtering and prediction theory; state estimation; tracking; transforms; Kalman filter; complementary subfilter; dimension; estimate error covariance; measurement vector; observer type subfilter; optimal reduced-order filter; state estimates; state vector; target tracking; two-step L-K transformation; Covariance matrix; Filtering; Finite impulse response filter; Noise measurement; Noise reduction; Observers; Satellites; Space stations; State estimation; Stochastic resonance;
Conference_Titel :
Aerospace and Electronics Conference, 1991. NAECON 1991., Proceedings of the IEEE 1991 National
Conference_Location :
Dayton, OH
Print_ISBN :
0-7803-0085-8
DOI :
10.1109/NAECON.1991.165782
