The current trend in high-accuracy aircraft navigation systems is towards using data from one or more inertial navigation
subsystem and one or more navigational reference subsystems. The enhancement in fault diagnosis and detection is
achieved via computing the minimum mean square estimate of the aircraft states using, for instance, Kalman filter
method. However, this enhancement might degrade if the cause of a subsystem fault has some effect on other subsystems
that are calculating the same measurement. One instance of such case is the tragic incident of Air France Flight 447 in
June, 2009 where message transmissions in the last moment before the crash indicated inconsistencies in measured
airspeed as reported by Airbus. In this research, we propose the use of mathematical aircraft model to work out the
current states of the airplane and in turn, using these states to validate the readings of the navigation equipment
throughout smart diagnostic decision tree network. Various simulated equipment failures have been introduced in a controlled environment to proof the concept of operation. The results have showed successful detection of the failing equipment in all cases.
The terrorist attack of 9/11 has revealed how vulnerable the civil aviation industry is from both
security and safety points of view. Dealing with several aircrafts cruising in the sky of a specific region
requires decision makers to have an automated system that can raise their situational awareness of how much
a threat an aircraft presents. In this research, an in-flight array of sensors has been deployed in a simulated
aircraft to extract knowledge-base information of how passengers and equipment behave in normal flighttime
which has been used to train artificial neural networks to provide real-time streams of normal
behaviours. Finally, a cascading of fuzzy logic networks is designed to measure the deviation of real-time
data from the predicted ones. The results suggest that Neural-Fuzzy networks have a promising future to
raise the awareness of decision makers about certain aviation situations.
In this paper the authors attempt to address the challenges of establishing situational awareness and subsequent decision
making regarding aircraft incidents. Using Smart Nodes and its state space model it is possible to create a live threat
profile and facilitate decision making in a net-centric environment. Smart Nodes are used to monitor parts of the
environment and by combining the sensors and information fusion, create a complex and useful picture of what is and
might occur. This information needs to be quickly and efficiently disseminated to operators. The authors present a novel
method of accomplishing this specifically designed for the air transportation industry.