Inverse Free Kalman Filter Using Approximate Inverse of Diagonally Dominant Matrices

Babu, K Subhash and Detroja, Ketan P (2019) Inverse Free Kalman Filter Using Approximate Inverse of Diagonally Dominant Matrices. IEEE Control Systems Letters, 3 (1). pp. 120-125. ISSN 2475-1456

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Conventional Kalman filter (KF) requires matrix inversion. But the pervasive applications of KF cannot at times afford inversion. Especially, embedded implementations do not have the capabilities to compute inverse using methods such as Cholesky decomposition. For large matrices, inversion could be computationally prohibitive even for non-embedded implementations. To address this problem, an inverse free Kalman filter (IFKF) is proposed in this letter. The inverse of innovation covariance matrix required in the update step of the KF is approximated using Taylor series expansion. The approximate inverse has a closed form expression in the elements of the original matrix. Bounds on the error covariance of proposed IFKF are also established. The proposed IFKF does not require any iterations to converge.

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Item Type: Article
Uncontrolled Keywords: Kalman filtering, estimation, filtering, sensor fusion
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 08 Aug 2018 09:50
Last Modified: 08 Aug 2018 09:50
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