TY - JFULL
AU - Nicholas D. Assimakis
PY - 2020/8/
TI - Kalman Filter Gain Elimination in Linear Estimation
T2 - International Journal of Computer and Information Engineering
SP - 235
EP - 241
VL - 14
SN - 1307-6892
UR - https://publications.waset.org/pdf/10011301
PU - World Academy of Science, Engineering and Technology
NX - Open Science Index 163, 2020
N2 - In linear estimation, the traditional Kalman filter uses the Kalman filter gain in order to produce estimation and prediction of the n-dimensional state vector using the m-dimensional measurement vector. The computation of the Kalman filter gain requires the inversion of an m x m matrix in every iteration. In this paper, a variation of the Kalman filter eliminating the Kalman filter gain is proposed. In the time varying case, the elimination of the Kalman filter gain requires the inversion of an n x n matrix and the inversion of an m x m matrix in every iteration. In the time invariant case, the elimination of the Kalman filter gain requires the inversion of an n x n matrix in every iteration. The proposed Kalman filter gain elimination algorithm may be faster than the conventional Kalman filter, depending on the model dimensions.
ER -