Title:
State Estimation using Gaussian Process Regression for Colored Noise Systems

dc.contributor.author Lee, Kyuman
dc.contributor.author Johnson, Eric N.
dc.contributor.corporatename Georgia Institute of Technology. School of Aerospace Engineering en_US
dc.date.accessioned 2017-06-23T13:00:41Z
dc.date.available 2017-06-23T13:00:41Z
dc.date.issued 2017-06
dc.description © 2017 IEEE en_US
dc.description.abstract The goal of this study is to use Gaussian process (GP) regression models to estimate the state of colored noise systems. The derivation of a Kalman filter assumes that the process noise and measurement noise are uncorrelated and both white. In relaxing those assumptions, the Kalman filter equations were modified to deal with the non-whiteness of each noise source. The standard Kalman filter ran on an augmented system that had white noises and other approaches were also introduced depending on the forms of the noises. Those existing methods can only work when the characteristics of the colored noise are perfectly known. However, it is usually difficult to model a noise without additional knowledge of the noise statistics. When the parameters of colored noise models are totally unknown and the functions of each underlying model (nonlinear dynamic and measurement functions) are uncertain or partially known, filtering using GP-Color models can perform regardless of whatever forms of colored noise. The GPs can learn the residual outputs between the GP models and the approximate parametric models (or between actual sensor readings and predicted measurement readings), as a member of a distribution over functions, typically with a mean and covariance function. Lastly, a series of simulations, including Monte Carlo results, will be run to compare the GP based filtering techniques with the existing methods to handle the sequentially correlated noise. en_US
dc.identifier.citation Lee, K., Johnson, E.N. "State Estimation using Gaussian Process Regression for Colored Noise Systems". Proceedings of IEEE 2017 Aerospace Conference. DOI: https://doi.org/10.1109/AERO.2017.7943781 en_US
dc.identifier.doi 10.1109/AERO.2017.7943781 en_US
dc.identifier.uri http://hdl.handle.net/1853/58390
dc.publisher Georgia Institute of Technology en_US
dc.publisher IEEE
dc.subject Colored noise en_US
dc.subject White noise en_US
dc.subject Kalman filters en_US
dc.subject Gaussian processes en_US
dc.title State Estimation using Gaussian Process Regression for Colored Noise Systems en_US
dc.type Text
dc.type.genre Post-print
dspace.entity.type Publication
local.contributor.author Johnson, Eric N.
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
relation.isAuthorOfPublication 175a1f2b-c14e-4c43-a9e5-136fb7f8e5d0
relation.isOrgUnitOfPublication a348b767-ea7e-4789-af1f-1f1d5925fb65
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