A New Approach for Modeling Correlated Gaussian Errors Using Frequency Domain Overbounding

Steven Langel, Omar García Crespillo, Mathieu Joerger

Abstract: This paper presents a new method to overbound Kalman filter (KF) based estimate error distributions in the presence of uncertain, time-correlated noise. Each noise component is a zero-mean Gaussian random process whose autocorrelation sequence (ACS) is stationary over the filtering duration. We show that the KF covariance matrix overbounds the estimate error distribution when the noise models overbound the Fourier transform of a windowed version of the ACS. The method is evaluated using covariance analysis for an example application in GPS-based relative position estimation.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 868 - 876
Cite this article: Langel, Steven, Crespillo, Omar García, Joerger, Mathieu, "A New Approach for Modeling Correlated Gaussian Errors Using Frequency Domain Overbounding," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 868-876.
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