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Applying modified householder transform to Kalman filter

Merchant, F and Vatwani, T and Chattopadhyay, A and Raha, S and Nandy, SK and Narayan, R and Leupers, R (2019) Applying modified householder transform to Kalman filter. In: 32nd International Conference on VLSI Design,, 5 January 2019 - 9 January 2019, New Delhi, pp. 431-436.

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Official URL: https://doi.org/10.1109/VLSID.2019.00092


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.

Item Type: Conference Paper
Publication: Proceedings - 32nd International Conference on VLSI Design, VLSID 2019 - Held concurrently with 18th International Conference on Embedded Systems, ES 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Air navigation; Embedded systems; Reconfigurable architectures; State estimation; VLSI circuits, Aircraft navigation; Coarse grained reconfigurable architecture; Computational finance; Index terms; Instruction level parallelism; Reconfigurable computing; Scientific applications; State of the art, Kalman filters
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 27 Dec 2022 07:07
Last Modified: 27 Dec 2022 07:07
URI: https://eprints.iisc.ac.in/id/eprint/78582

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