Titlebook: Kalman Filtering; with Real-Time Appli Charles K. Chui,Guanrong Chen Textbook 19993rd edition Springer-Verlag Berlin Heidelberg 1999 Filter

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in Berlin gehalten. Er wies in dieser darauf hin, dass „zwei Jahrzehnte nach Ende des Kalten Krieges verstrichen sind, die für die Gestaltung einer neuen Weltordnung nicht oder nicht ausreichend genutzt wurden“. Gerade die junge Generation wird gefordert sein, in der ersten Hälfte des 21. Jahrhunder

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Orthogonal Projection and Kalman Filter,easily understood to be a least-squares estimate of X. with the properties that (i) the transformation that yields . from the data . is linear, (ii) . is unbiased in the sense that ., and (iii) it yields a minimum variance estimate with . as the optimal weight. The disadvantage of this elementary ap

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Correlated System and Measurement Noise Processes,ve assumed all along that . for ., . = 0,1,.... However, in applications such as aircraft inertial navigation systems, where vibration of the aircraft induces a common source of noise for both the dynamic driving system and onboard radar measurement, the system and measurement noise sequences {..} a

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obligation

Extended Kalman Filter and System Identification, procedure is usually performed in deriving the filtering equations. We will consider a real-time linear Taylor approximation of the system function at the previous state estimate and that of the observation function at the corresponding predicted position. The Kalman filter so obtained will be call

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Decoupling of Filtering Equations,l-time. However, if the state vector has a very high dimension ., and only a few components are of interest, this filter gives an abundance of useless information, an elimination of which should improve the efficiency of the filtering process. A decoupling method is introduced in this chapter for th

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Wavelet Kalman Filtering,ng performed in the time domain. Among them, perhaps the most exciting ones are wavelet algorithms, which are useful tools for multichannel signal processing (e.g., estimation or filtering) and multiresolution signal analysis. This chapter is devoted to introduce this effective technique of wavelet

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Notes, real-time applications. No attempt was made to cover all the rudiment of the theory, and only a small sample of its applications has been included. There are many texts in the literature that were written for different purposes, including Anderson and Moore (1979), Balakrishnan (1984,87), Brammer a