@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},
	}