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 language||English (US)|
|Title of host publication||2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||12|
|State||Published - Jun 5 2018|
|Event||2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Monterey, United States|
Duration: Apr 23 2018 → Apr 26 2018
|Name||2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Proceedings|
|Other||2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018|
|Period||4/23/18 → 4/26/18|
Bibliographical noteFunding 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.
© 2018 IEEE.
- Flight Maneuverability
- Kalman Filter Estimation
- Synthetic Air Data