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Kalman filtering: with real-time applications pdf

Kalman filtering: with real-time applications. Charles K. Chui, Guanrong Chen

Kalman filtering: with real-time applications

ISBN: 3540878483,9783540878483 | 239 pages | 6 Mb

Download Kalman filtering: with real-time applications

Kalman filtering: with real-time applications Charles K. Chui, Guanrong Chen
Publisher: Springer

IP: 128.244.244.* [4][游客]coolboy 2008-9-13 05:49. Kalman Filtering: with Real Time Application. Second Edition, Springer-Verlag, New York, 195 pp. [5] Gelb, A., Applied Optimal Estimation, The MIT Press, Massachusetts Institute of. For a long time, the least-squares (LS) estimation problem in linear stochastic systems from measurements perturbed by additive noises has received considerable attention in the scientific community due to its wide applicability in many practical As in the Kalman filter, independent white noises are considered in all the mentioned papers; however, this assumption may not be realistic and can be a limitation in many real-world problems in which noise correlation may be present. For example, highly automated agile manufacturing, command, control and communications, and distributed real-time multimedia applications all operate over long lifetimes and in highly non-deterministic environments. Language: English Released: 2009. Because any calculations would Whereas the Kalman filter makes a single prediction at each point in time, then adjusts it using the observed data, a particle filter uses simulations to make a large number of predictions (the particles) at each point in time. Chen, 1991: Kalman Filtering with Real-Time Applications. Although superior to Kalman filters, particle filters have higher computational requirements, which limits practical use in real-time applications. Kalman Filtering, John Wiley and Sons, New York, 1993. GO Kalman Filtering with Real-Time Applications Author: NO Type: eBook. Publisher: Springer Page Count: 240. As well as producing accurate estimates, the Kalman filter could run in real time: all it needed to generate an estimate were the previous prediction and current onboard measurement.

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