AIRCRAFT CLASSIFICATION AND NOISE MAP ESTIMATION BASED ON REAL-TIME MEASUREMENTS OF TAKE-OFF NOISE

This paper summarizes a new methodology about aircrafts identification and the generation of estimated noise map based on real time noise measurement for each take-off. The data acquisition is made at 50 Ks/s and 24 bits, during 24 seconds of aircraft take-off. The aircraft identification is made through two parallel neural networks combined with a weighted addition. In order to generate the inputs to the neural networks, the features were obtained from the auto-regressive (AR) model and the 1/12 octave analysis. This system has 13 categories of aircrafts and has an identification level above 84% in real environments. Noise signals generated during aircraft take-off are measured in a fixed location on the airport runway end using a linear 4-microphone array. The noise map is made for each take-off and presents four layers related to four time intervals of take-off. Each time interval is represented by an equivalent point sound source location based on estimation of time-difference-of-arrival (TDOA) of the acoustic wave of aircraft taking-off.