A multiple hypothesis prediction method for improved aircraft state awareness

Flight crew´s decision making has historically been identified as one of the leading contributing and causal factors in aircraft accidents. As both the avionics systems inside cockpit and the aviation operational procedures are becoming increasingly complex, the flight crew´s unawareness of the aircraft state is an even more serious risk. Meanwhile, the ever-increasing automation level inside cockpit has further removed flight crews from traditional flight duties and their over-reliance on automation urges effective information management methods and architectures as well as alerting systems to provide improved aircraft state awareness. This paper addresses this issue by presenting a Multiple Hypothesis Prediction (MHP) method. The MHP method 1) integrates all the onboard sensors and information sources with a stochastic estimator to obtain an accurate and reliable estimate of the aircraft state, 2) predicts ahead the aircraft state vector, which includes flight critical elements such as airspeed, flight path angle, and altitude, based on the current aircraft dynamic model, current flight mode, and alternative flight modes, 3) compares the predicted state with a set of pre-defined rules, 4) and issues an alert as well as suggested actions to the flight crew based on hypothetical flight mode transitions if any of the pre-defined rules are violated. This paper summarizes the objectives and activities of a Flight Deck Information Management (FDIM) and the role of the MHP method within FDIM, introduces the algorithms of the MHP method, and demonstrates its functionality with example hazardous flight scenarios.