Abstract
The optimal filter computes the posterior probability distribution of the state in a dynamical system, given noisy measurements, by iterative application of prediction steps according to the dynamics of the state, and correction steps taking the measurements into account. A new class of approximate nonlinear filter has been recently proposed, the idea being to produce a sample of independent random variables, called a particle system, (approximately) distributed according to this posterior probability distribution. The method is very easy to implement, even in high-dimensional problems, since it is sufficient in principle to simulate independent sample paths of the hidden dynamical system.
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© 2001 Springer Science+Business Media New York
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Musso, C., Oudjane, N., Le Gland, F. (2001). Improving Regularised Particle Filters. In: Doucet, A., de Freitas, N., Gordon, N. (eds) Sequential Monte Carlo Methods in Practice. Statistics for Engineering and Information Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3437-9_12
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DOI: https://doi.org/10.1007/978-1-4757-3437-9_12
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-2887-0
Online ISBN: 978-1-4757-3437-9
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