@article{(Open Science Index):https://publications.waset.org/pdf/10011301, title = {Kalman Filter Gain Elimination in Linear Estimation}, author = {Nicholas D. Assimakis}, country = {}, institution = {}, abstract = {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. }, journal = {International Journal of Computer and Information Engineering}, volume = {14}, number = {7}, year = {2020}, pages = {236 - 241}, ee = {https://publications.waset.org/pdf/10011301}, url = {https://publications.waset.org/vol/163}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 163, 2020}, }