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Non-linear filtering based on observations from Gaussian processes
Date
2011-03-12
Author
Gustafsson, Fredrik
Saha, Saikat
Orguner, Umut
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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We consider a class of non-linear filtering problems, where the observation model is given by a Gaussian process rather than the common non-linear function of the state and measurement noise. The new observation model can be considered as a generalization of the standard one with correlated measurement noise in both time and space. We propose a particle filter based approach with a measurement update step that requires a memory of past observations which can be truncated using a moving window to obtain a finite-dimensional filter with arbitrarily good accuracy. The validity of the conceptual solution is proved via simulations on a one dimensional tracking problem and implementation issues are discussed.
Subject Keywords
Filtering Algorithms
,
Noise
,
Filtering
URI
https://hdl.handle.net/11511/47993
DOI
https://doi.org/10.1109/aero.2011.5747440
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
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F. Gustafsson, S. Saha, and U. Orguner, “Non-linear filtering based on observations from Gaussian processes,” 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47993.