Lainiotis Information Filter

—Kalman filter and Lainiotis filter are well known algorithms that solve the filtering problem, producing the state estimation as well as the corresponding estimation error covariance matrix. Using the Information estimation error covariance matrix, which is the inverse of the estimation error covariance matrix, the Information filter has been derived from Kalman filter. In this paper, using the Information estimation error covariance matrix, the Lainiotis Information filter is introduced. The Lainiotis filter and the Lainiotis Information filter are equivalent with respect to their behavior, since they produce the same estimations. The computational requirements of the Lainiotis filter and the Lainiotis Information filter are determined and a method is proposed to a-priori (before the filters' implementation) decide which filter is the faster one. In the time invariant systems case, the Lainiotis Information filter provides a faster method than the classical one to solve the Riccati equation emanating from Lainiotis filter.