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

Steven Langel, Omar Garcia Crespillo, Mathieu Joerger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages868-876
Number of pages9
ISBN (Electronic)9781728102443
DOIs
StatePublished - Apr 2020
Externally publishedYes
Event2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020 - Portland, United States
Duration: Apr 20 2020Apr 23 2020

Publication series

Name2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020

Conference

Conference2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
Country/TerritoryUnited States
CityPortland
Period4/20/204/23/20

Keywords

  • Colored noise
  • Fourier transform
  • Kalman filter
  • covariance matrix
  • overbound
  • positive sequence

ASJC Scopus subject areas

  • Signal Processing
  • Aerospace Engineering
  • Control and Optimization
  • Instrumentation

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