A parallel square-root algorithm and its systolic array implementation are proposed for performing modified extended Kalman filtering (MEKF). The proposed parallel square-root algorithm is designed based on the singular value decomposition (SVD) and the Faddeev algorithm, and a very large scale integration (VLSI) systolic array architecture is developed for its implementation. Compared to other square root Kalman filtering algorithms, the proposed method is more numerically stable. The VLSI architecture described has good parallel and pipelining characteristics in applying to the MEKF and achieves higher efficiency. For n-dimensional state vector estimations, the proposed architecture consists of O(2n/sup 2/) processing elements and uses O((s+17)n) time-steps for a complete iteration at each instant, in contrast to the complexity of O((s+6)n/sup 3/) time-steps for a sequential implementation, where s approximately=log n.<>
A parallel square-root algorithm for modified extended Kalman filter
IEEE Transactions on Aerospace and Electronic Systems ; 28 , 1 ; 153-163
1992-01-01
779508 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Square-Root Unscented Schmidt–Kalman Filter
AIAA | 2017
|Square-Root Unscented Schmidt-Kalman Filter
Online Contents | 2017
|Extended Kalman Filter (EKF) Algorithm
NTIS | 1989
|A square root formulation of the Kalman- Schmidt filter
AIAA | 1967
|A square root formulation of the Kalman- Schmidt filter.
AIAA | 1967
|