Non-linear filtering based on observations from Gaussian processes

Gustafsson, Fredrik
Saha, Saikat
Orguner, Umut
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.


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Citation Formats
F. Gustafsson, S. Saha, and U. Orguner, “Non-linear filtering based on observations from Gaussian processes,” 2011, Accessed: 00, 2020. [Online]. Available: