A parallel square-root algorithm for modified extended Kalman filter

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.<
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