Attitude estimation with feedback particle filter

Chi Zhang, Amirhossein Taghvaei, Prashant G. Mehta

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


This paper presents theory, application, and comparisons of the feedback particle filter (FPF) algorithm for the problem of attitude estimation. The paper builds upon our recent work on the exact FPF solution of the continuous-time nonlinear filtering problem on matrix Lie groups. In this paper, the details of the FPF algorithm are presented for the problem of attitude estimation - a nonlinear filtering problem on SO(3). Quaternions are employed for computational purposes. The algorithm requires a numerical solution of the filter gain function, and two methods are applied for this purpose. Comparisons are also provided between the FPF and some popular algorithms for attitude estimation, including the multiplicative EKF, the unscented quaternion estimator, the invariant EKF, and the invariant ensemble Kalman filter. Simulation results are presented that help illustrate the comparisons.

Original languageEnglish (US)
Title of host publication2016 IEEE 55th Conference on Decision and Control, CDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509018376
StatePublished - Dec 27 2016
Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
Duration: Dec 12 2016Dec 14 2016

Publication series

Name2016 IEEE 55th Conference on Decision and Control, CDC 2016


Other55th IEEE Conference on Decision and Control, CDC 2016
Country/TerritoryUnited States
CityLas Vegas

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Control and Optimization


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