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Monster Optimisation Environment (MOE)

The MOE (Monster Optimisation Environment) is a SW tool part of the MONSTER noise monitoring system and is available as a stand alone SW programme. The MOE is a module of the MONSTER system, dedicated to the development of an innovative approach for the exploitation of noise information measured by remote monitoring stations scattered over a given airport control area, finalised to the concurrent maximisation of the use of such information and the optimisation of the deployment layout of remote monitoring stations targeting primarily the minimisation of their number. In the framework of the MONSTER research project, the MOE has been devised as one of the most innovative items, and its development has been based on the implementation of a novel approach for the exploitation of the information made available by a number of remote noise monitoring stations.

Given an airport area to be monitored, it is usually of interest the construction of time averaged noise indexes, like the SEL (sound exposure level) for specific points or for the whole area, or the measure of peak noise levels in specific points, which can be used to blame airplanes not respecting flight traffic gates allowed by airport authorities for noise abatement reasons.

The finalities of the MOE consist in optimising sensors (microphones) deployment over the territory aimed at airplane noise monitoring, according to the following objectives:
- Reduce the number of microphones.
- Use where acceptable microphones of reduced performance (measurement accuracy).

The type of optimisation implemented, is clearly related to the approach used to exploit noise measures and to generate time averaged noise indexes. For this reason microphones deployment optimisation and the mathematical engine used to correlate microphones readings with airplane position are both developed in the MOE. The MOE incorporates one further and very innovative functionality, i.e. the capability to use the information about noise recorded by microphones in order to derive information about the position occupied by the noise source (the airplane) and generate related route; this function is very important because the approach in reducing noise levels around airports consists in limiting the access to and from runway in specific gates implementing noise abatement routes. This capability of the MOE system therefore allows to blame not complying planes on the basis of the information by microphones only.

The airplane generates a volume where its noise effects are present, in motion with it, and correlated to its position. Once we are able to build this volume starting from a reduced dataset from microphones, we have information about airplane position.

The MOE proposes to experiment a very innovative approach to deal with the problem of airport noise monitoring; its mathematical core has been designed in order to implement a twofold function:
- Off line design tool to design a microphones deployment layout including the lowest number of stations, exploiting the MOE mathematical module to perform tasks.

- On line airplane localisation and generation of noise maps extrapolation of the dataset achieved by a reduced number of microphones, to generate noise information over the whole control area

At a given instant, microphones measure noise levels generated by the flight of a given airplane. Noise propagation algorithms are included in the MOE and are reverted to identify noise sources compatible with the noise measured; algorithms integrate meteorological and wind information recorded by the Remote Terminal Units; algorithms embed approximated effects due to the fact that the noise source is in motion and reflection effects from the ground.

This set of measures, performed by a limited number of microphones at a given instant, does not necessarily correspond univocally to one position of the sound source (the airplane); multiple mathematical solutions are possible; in order to restrict the number of solutions the MOE implements the following:
- Boundary conditions are placed in the design stage of the airport monitoring system in order to restrict the range of solutions; for instance, at a given distance from the runway, an approaching airplane can be found in a limited range of heights from the ground.

- Noise measures are repeated in successive time instants, further reducing the number of possible solutions until convergence is achieved
Once the noise source is localised, the same noise propagation algorithms are used to estimate noise levels in every requested point of the control surface; time averaged noise indexes, like the SEL, are then easily generated. Information is then exchanged with the MONSTER main SW application, providing estimated airplane route and noise indexes. The system can calculate if the airplane flight track complies or not with noise abatement routes.

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