A Reduced-Order Extended Kalman Filter by Local Minimization of the Error Covariance

dc.contributor.author	Strandt, Andrew
dc.contributor.author	Strandt, Alia
dc.contributor.author	Yaz, Edwin
dc.date.accessioned	2026-08-23T07:59:19Z
dc.date.available	2026-08-23T07:59:19Z
dc.date.copyright	2026/04/01
dc.date.issued	2026
dc.identifier.issn	2689-6117
dc.identifier.other	aldsc-25-1062.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4315909
dc.description.abstract	Abstract. This work introduces a new reduced-order extended Kalman filter (REKF) for discrete nonlinear dynamic systems, which is derived by least-square minimization of the local estimation error covariance. Both the filter and the one-step ahead predictor forms are derived. For demonstration purposes, the REKF is applied to a polynomial nonlinear system, and its estimation error is compared to that of the full-order extended Kalman filter.
dc.publisher	The American Society of Mechanical Engineers (ASME)
dc.title	A Reduced-Order Extended Kalman Filter by Local Minimization of the Error Covariance
dc.type	Journal Paper
dc.journal.volume	6
dc.journal.issue	2
dc.journal.title	ASME Letters in Dynamic Systems and Control
dc.identifier.doi	10.1115/1.4070172
dc.journal.fristpage	35
dc.journal.lastpage	45
dc.page	11
dc.tree	ASME Letters in Dynamic Systems and Control:;2026:;volume( 006 ):;issue:002
dc.contenttype	Fulltext