The Order-Recursive Chandrasekhar Equations for Fast Square-Root Kalman Filtering View Full Text


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Chapter Info

DATE

1993

AUTHORS

D. T. M. Slock

ABSTRACT

It has been three decades now that Kaiman filtering has been the tool of choice for least-squares estimation in linear state-space models. The Kaiman filter makes no distinction between time-varying and time-invariant models. About two decades ago, Kailath introduced the Chandrasekhar equations, which generally reduce the computational complexity of the Kaiman filter when the linear state-space model is time-invariant. Along another venue, the so-called square-root forms of the Kaiman filter (SRCF, SRIF) were introduced, which propagate a matrix square-root of the covariance matrix of the state estimation error. In this way, the covariance matrix itself, being the square of its square-root, is inherently non-negative definite and symmetric at all times. The square-root algorithms offer various numerical advantages [2] and have a similar algorithmic complexity as their Riccati equation based counterpart(s). More... »

PAGES

419-420

Book

TITLE

Linear Algebra for Large Scale and Real-Time Applications

ISBN

978-90-481-4246-0
978-94-015-8196-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-94-015-8196-7_53

DOI

http://dx.doi.org/10.1007/978-94-015-8196-7_53

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038223908


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