A Parallel Square-Root Algorithm for Modified Extended Kalman Filter

A parallel square-root algorithm together with its systolic arrays implementation are proposed for performing the modified extended Kalman filter (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 arrays architecture is developed for its implementation. Comparing with other square root Kalman filtering algorithms existing in the literature, the proposed method is more numerically stable. Moreover, the new VLSI architecture has very nice 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(2n²) 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)n³) time-steps for a sequential implementation, where as ≈ log n. © 1992 IEEE