GNSS/INS based estimation of air data and wind vector using flight maneuvers

Kerry Sun, Christopher D. Regan, Demoz Gebre Egziabher

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

3 Scopus citations

Abstract

We develop a GNSS/INS based algorithm to estimate air data (angle-of-attack and sideslip angle) and wind vector in real time. Our work uses observability analysis to demonstrate the feasibility of the model-free synthetic air data estimation. In addition, we show that certain canonical flight maneuvers, defined by the aircraft's orientation and airspeed, result in a high degree of observability for the air data parameters and the wind velocity vector estimates. Furthermore, a lower bound on the average time of wind variation can be derived from the analysis. Finally, the GNSS/INS-based algorithm is tested to estimate these parameters using simulation data. Results of simulation are consistent with the observability analysis.

Original languageEnglish (US)
Title of host publication2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages838-849
Number of pages12
ISBN (Electronic)9781538616475
DOIs
StatePublished - Jun 5 2018
Event2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Monterey, United States
Duration: Apr 23 2018Apr 26 2018

Publication series

Name2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Proceedings

Other

Other2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018
Country/TerritoryUnited States
CityMonterey
Period4/23/184/26/18

Bibliographical note

Funding Information:
The authors would like to thank NASA (via Grant NNX15AV67G), UMN OVPR MnDRIVE Initiative, Sentera LLC. and LMCCR Legislature of the State of Minnesota for supporting this work. This work was part of the ongoing project called ”A Rapid Autonomy Platform Testbed Reconfiguration Suite” (RAPTRS) sponsored by NASA Ames Research Center.

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Flight Maneuverability
  • Kalman Filter Estimation
  • Observability
  • Synthetic Air Data

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