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

4 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
CountryUnited States
CityMonterey
Period4/23/184/26/18

Keywords

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

Fingerprint Dive into the research topics of 'GNSS/INS based estimation of air data and wind vector using flight maneuvers'. Together they form a unique fingerprint.

Cite this