A Comparison of Discrete Linear Filtering Algorithms
- 1 January 1973
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Aerospace and Electronic Systems
- Vol. AES-9 (1), 28-37
- https://doi.org/10.1109/taes.1973.309697
Abstract
Seven filter algorithms were presented in a recent survey paper [2], and were compared computationally (operations count) when relatively few observations were to be processed. These algorithms are elaborated further in this paper. Details of the computations are presented, and it is shown that for problems with even moderately large amounts of data, the information matrix and square-root information matrix formulations are computationally more efficient than the other methods considered (conventional Kalman, stabilized Kalman, and square-root covariance mechanizations). It is pointed out that Schmidt's matrix factorization-Householder transformation technique leads to the same equations as those obtained via Potter's method. Several improvements in the equation mechanization are given.Keywords
This publication has 4 references indexed in Scilit:
- Discrete square root filtering: A survey of current techniquesIEEE Transactions on Automatic Control, 1971
- On computational efficiency of linear filtering algorithmsAutomatica, 1971
- Extensions and applications of the Householder algorithm for solving linear least squares problemsMathematics of Computation, 1969
- A New Approach to Linear Filtering and Prediction ProblemsJournal of Basic Engineering, 1960