Comparison of aircraft state prediction methods under sensor uncertainty

The paper discusses a comparison of various aircraft state prediction methods in the presence of sensor uncertainty. Aircraft state prediction and, specifically, energy state prediction is an important step in providing the flight crew with visual and aural cues to improve their airplane state awareness (ASA) and, thus, increase aviation safety as the lack of aircraft state awareness has been one of the leading causal and contributing factors in aviation accidents. This paper focusses on predictive alerting methods to predict (a) stall and overspeed conditions, (b) high-and-fast conditions, and (c) automation mode transitions. The proposed method estimates and subsequently predicts the aircraft state based on (i) aircraft state related information output by the onboard avionics, (ii) the configuration of the aircraft, (iii) appropriate aircraft dynamics models of both the active modes and the modes to which can be transitioned via simple pilot actions, and (iv) accurate models of the uncertainty of the dynamics and sensors. To compare the performance of the various methods, this paper analyzed flight data collected during a recent NASA flight simulator study in which eleven commercial airline crews (22 pilots) completed more than 230 flights.