Renormalization group flow in k-space for nonlinear filters, Bayesian decisions and transport

We derive a new algorithm which avoids normalization of the probability density for particle flow. The algorithm was inspired by renormalization group flow in quantum field theory. In contrast with other particle flow algorithms, this one works in k-space rather than state space. We have roughly 30...

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Veröffentlicht in:	2015 18th International Conference on Information Fusion (Fusion) S. 1617 - 1624
Hauptverfasser:	Daum, Fred, Jim Huang
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ISIF 01.07.2015
Schlagworte:	Accuracy Approximation methods extended Kalman filter Fourier transforms Mathematical model Monge-Kantorovich optimal transport nonlinear filter Nonlinear filters particle filter particle flow transport problem Weather forecasting
Online-Zugang:	Volltext
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