Titlebook: Advances in Estimation, Navigation, and Spacecraft Control; Selected Papers of t Daniel Choukroun,Yaakov Oshman,Moshe Idan Conference proce

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Stephen K. Scott,Kenneth Showalter modeling of the sensors errors as state-multiplicative noise processes. Such modeling allows application of a State Multiplicative Kalman Filter and is shown to outperform the standard Kalman filter based on ad-hoc analysis ignoring the state-multiplicative noise. The design considerations include

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ATOPY

定點

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https://doi.org/10.1007/978-3-642-72279-0A recursive filter for polynomial systems is derived, where the bound on the mean square estimation error is explicitly calculated. The derivation relies on the recently introduced theory of positive polynomials. The general form of the filter is similar to the extended Kalman filter, but the filter gain is calculated differently.

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myelography

J. Korpij?rvi,E. Laine,H. Ahlstedtped. Finally, the vector stated estimator is derived by recursively propagating the characteristic function of the unnormalized conditional pdf through measurement updates and dynamic state propagation. The conditional mean and variance are easily computed from the first and second derivatives of this characteristic function.

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法律的瑕疵

D. H. Bache,E. Rasool,D. Moffat,M. Johnsonflight in which the GPS measurements and relative positions are exchanged among neighboring network nodes. The novel technique yields similar performance to the reiterated Kalman filtering, which is the optimal linear Gaussian solution, while demanding less storage capacity and computational throughput in the problems of interest.