Abstract

The main goal of this project is the study and modeling of the noise annoyance caused by air traffic by using the fuzzy logic theory. Like many other environmental problems, air traffic noise, continues to grow and has become a serious problem in many countries. Millions of people living or working around airport areas can suffer from noise exposure effects as for instance hearing loss, interference with communication, stress, sleep disturbance, psychological effects as well as a general reduction in quality of life and tranquillity. However, noise annoyance is a difficult issue to evaluate as it is open to subjective reactions. Fuzzy logic theory is the perfect tool to analyse and evaluate all that vague and imprecise concepts that contrary to many other concepts, like age, distance or time, can not be measured as easily. In this project, a fuzzy function has been developed to quantify the annoyance level that people living or working in areas near airports are suffering. The parameters that come into play in this feature are the noise level, time of day, the number of events per hour, and finally, if it is a residential or an industrial area. Finally, the results of the fuzzy system have been represented on graphics that show the levels of nuisance caused by aircraft noise in each of the situations according the variables of the fuzzy function.

Abstract

traffic predictability is paramount in the air traffc system in order to enable concepts such as Trajectory Based Operations (TBO) and higher automation levels for self-separation. Whereas in simulated environments 4D conflict-free trajectory optimisation has shown good potential in the improvement of air traffc effciency, its application to real operations has been very challenging due to the current lack of information sharing between airspace users. Consequently, such operations are still very limited in scope and rarely attempted in dense traffic situations. Better predictability of other traffc future states would be an enabler for each aircraft to fly its user preferred route without decreasing safety in a self-separation context. But this is not an easy task when basic aircraft parameters such as aircraft weight, performance data or airline strategies are not available at the time of prediction. In this paper the authors propose to compensate this hindrance by continuously integrating the state of the surounding traffc to improve the ownship's knowledge of other aircraft's dynamics. Specifically, conventional position (and velocity) messages, as coming from Automatic Dependent Surveillance Broadcast (ADS-B), are integrated at the ownship. Then, an optimisation problem is formulated, using optimal control theory, that minimises the error with the known states, having the parameters of study (i.e. mass) as decision variables. A scenario with two departing trajectories is used to demonstrate the e ectiveness of this parameter estimation method. In it, the take-off mass of the potential intruder is estimated on- board the ownship and its impact to conflict detection and resolution is presented, demonstrating the big improvements in predictability and safety. Peer Reviewed Document type: Conference object