Titlebook: Kalman Filtering with Real-Time Applications; Charles K. Chui,Guanrong Chen Textbook 19871st edition Springer-Verlag Berlin Heidelberg 198

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Charles K. Chui,Guanrong Cheng für die fG herauszuarbeiten, die soweit wie nötig die Vielfalt der Verfahrenstypen berücksichtigt, soweit aber wie möglich einheitliche Verfahrensregeln aufweist und insgesamt ein festes Gerippe für den Verfahrensablauf bietet, das zwischen Zweckmäßigkeit und Rechtsstaatlichkeit im gleichen Maße a

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Notes,ailed treatment we have had to omit many important topics; some of these will be introduced very briefly in this chapter. The interested reader is referred to the modest list of references in this text for further study.

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Textbook 19871st editionter gives a linear, unbiased, and min­ imum error variance recursive algorithm to optimally estimate the unknown state of a dynamic system from noisy data taken at discrete real-time intervals. It has been widely used in many areas of industrial and government applications such as video and laser tr

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Decoupling of Filtering Equations,his purpose. It allows us to decompose an .-dimensional limiting Kalman filtering equation into n independent one-dimensional recursive for mulas so that we may drop the ones that are of little interest.

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Kalman Filter: An Elementary Approach,or with the optimal weight, using all available data information. The filtering equations are first obtained for a system with no deterministic (control) input. By superimposing the deterministic solution, we then arrive at the general Kalman filtering equations.

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Colored Noise,der the assumption that . where . and . being uncorrelated zero mean Gaussian white noises with . and .. and .. being known .×. and .×. constant matrices. The noise sequences . and . satisfying (i) and (ii) will be called .. This chapter is devoted to the study of Kalman filtering with this assumption on the noise sequences.