Inverse Free Kalman Filter Using Approximate Inverse of Diagonally Dominant Matrices

Autor:	Ketan P. Detroja, K. Subhash Babu
Rok vydání:	2019
Předmět:	Control and Optimization Covariance matrix MathematicsofComputing_NUMERICALANALYSIS Inverse Kalman filter Covariance symbols.namesake Matrix (mathematics) Control and Systems Engineering Taylor series symbols Applied mathematics Cholesky decomposition Diagonally dominant matrix Mathematics
Zdroj:	IEEE Control Systems Letters. 3:120-125
ISSN:	2475-1456
DOI:	10.1109/lcsys.2018.2854238
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a3424cff8eff7050805d7f4d2c3f0c39 https://doi.org/10.1109/lcsys.2018.2854238 Zobrazit plný text záznamu