Applying modified householder transform to Kalman filter

Kalman filter (KF) is a key operation in many engineering and scientific applications ranging from computational finance to aircraft navigation. Recently, there have been proposals in the literature for acceleration of KF using modified Faddeeva algorithm (MFA) where the classical Householder transform (HT) is used in implementation of MFA on a custimizable platform called REDEFINE. REDEFINE is a coarse-grained reconfigurable architecture that has capabilities of recomposing data-paths at run-time and on-demand. In this paper, we present realization of KF using MFA where we implement MFA using modified Householder transform (MHT) presented in the literature. We call this as M2FA. It is shown that the implementation of KF using M2FA clearly outperforms the implementation of KF using MFA on REDEFINE and also the realization of KF on REDEFINE is scalable. Performance improvements over state-of-the-art implementations are also discussed.