KEYNOTE - Predictive Alerting to Improve Aircraft State Awareness Using GNSS, Inertial, and Air Data

Pengfei Duan and Maarten Uijt de Haag

Abstract:	The lack of aircraft state awareness has been one of the leading causal and contributing factors in aviation accidents. Many of these accidents were due to flight crew’s inability to understand the automation modes and properly monitor the aircraft energy and attitude state. The capability of providing flight crew with improved aircraft state awareness is essential in ensuring aviation safety. The aircraft state described in this paper includes energy state, attitude state, and system mode state. Most of the elements in these states can be measured by onboard navigation systems such as Global Navigation Satellites Systems (GNSS), Inertial, and Air Data. This paper describes a predictive alerting method that uses Multiple Hypothesis Prediction (MHP) based on available aircraft avionics outputs. A speed reversion scenario is used to demonstrate the functionality of the MHP method in reducing the occurrence of the vertical navigation (VNAV) function mode confusion.
Published in:	Proceedings of the ION 2015 Pacific PNT Meeting April 20 - 23, 2015 Marriott Waikiki Beach Resort & Spa Honolulu, Hawaii
Pages:	659 - 673
Cite this article:	Duan, Pengfei, de Haag, Maarten Uijt, "KEYNOTE - Predictive Alerting to Improve Aircraft State Awareness Using GNSS, Inertial, and Air Data," Proceedings of the ION 2015 Pacific PNT Meeting, Honolulu, Hawaii, April 2015, pp. 659-673.
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