TY - GEN
T1 - Aircraft class recognition based on dynamic hierarchical weighting of multiple neural networks outputs
AU - Sanchez-Perez, Luis Alejandro
AU - Sanchez-Fernandez, Luis Pastor
AU - Suarez-Guerra, Sergio
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/18
Y1 - 2015/12/18
N2 - Aircraft noise is a major concern for current world-wide airports. Evaluation of airport noise pollution mainly depends on the correlation between the aircraft class, the noise measured and the flight path. Certification, evaluation and regulation procedures usually require the foregoing correlation to be performed by means of different sources of information beyond that provided by the aircraft itself. In this regard, methods to identify the aircraft class taking off based on features extraction from the noise signal have been developed. This paper introduces a new model for aircraft class recognition based on signal segmentation and dynamic hierarchical weighting of Κ parallel neural networks outputs Op. Performance of new model is benchmarked against models in literature over a database containing real-world take-off noise measurements using three different features types. The new model is more accurate regarding the abovementioned database and successfully classifies 87% of measurements.
AB - Aircraft noise is a major concern for current world-wide airports. Evaluation of airport noise pollution mainly depends on the correlation between the aircraft class, the noise measured and the flight path. Certification, evaluation and regulation procedures usually require the foregoing correlation to be performed by means of different sources of information beyond that provided by the aircraft itself. In this regard, methods to identify the aircraft class taking off based on features extraction from the noise signal have been developed. This paper introduces a new model for aircraft class recognition based on signal segmentation and dynamic hierarchical weighting of Κ parallel neural networks outputs Op. Performance of new model is benchmarked against models in literature over a database containing real-world take-off noise measurements using three different features types. The new model is more accurate regarding the abovementioned database and successfully classifies 87% of measurements.
KW - aircraft class
KW - dynamic hierarchical weighting
KW - neural networks
KW - pattern recognition
KW - real-world measurements
KW - signal segmentation
KW - take-off noise
UR - http://www.scopus.com/inward/record.url?scp=84962721116&partnerID=8YFLogxK
U2 - 10.1109/IntelliSys.2015.7361186
DO - 10.1109/IntelliSys.2015.7361186
M3 - Contribución a la conferencia
AN - SCOPUS:84962721116
T3 - IntelliSys 2015 - Proceedings of 2015 SAI Intelligent Systems Conference
SP - 499
EP - 506
BT - IntelliSys 2015 - Proceedings of 2015 SAI Intelligent Systems Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - SAI Intelligent Systems Conference, IntelliSys 2015
Y2 - 10 November 2015 through 11 November 2015
ER -