Military aircrafts’ classification based on their sound signature

Purpose – This paper aims to present a system framework for classifying different models of military aircrafts, which is based on the sound they produce. Design/methodology/approach – The technique is based on extracting a compact feature set, of only two features, extracted from the frequency domain of the aircrafts’ sound signals produced by their engines, namely, the spectral centroid and the signal bandwidth. These features are then introduced to an artificial neural network to classify the aircraft signals. Findings – The current system identifies the aircraft type among four military aircrafts: Mirage 2000, F-16 Fighting Falcon, F-4 Phantom II and F-104 Starfighter. The experimental results show that the aforementioned types of aircrafts can be accurately classified up to 96.2 per cent via the proposed method. Practical implications – The proposed system can be used as a low-cost assistive tool to the already existing radar systems to avoid cases of missed detection or false alarm. More importantly,...

[1]  Marwan Bikdash,et al.  Spectral features for the classification of civilian vehicles using acoustic sensors , 2009, 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems.

[2]  Luis Alejandro Sánchez-Pérez,et al.  Aircraft take-off noises classification based on human auditory’s matched features extraction , 2014 .

[3]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[4]  Manuel Recuero,et al.  Real-time aircraft noise likeness detector , 2010 .

[5]  M. Rangoussi,et al.  Feature Extraction for Audio Classification of Gunshots Using the Hartley Transform , 2012 .

[6]  Preeti Rao,et al.  VEHICLE ENGINE SOUND ANALYSIS APPLIED TO TRAFFIC CONGESTION ESTIMATION , 2011 .

[7]  Alejandro Osses,et al.  Noise Classification of Aircrafts using Artificial Neural Networks , 2012 .

[8]  Liu Feng,et al.  Short-Wave Aviation Communication Signal Analysis and Aircraft Classification , 2008, 2008 International Multi-symposiums on Computer and Computational Sciences.

[9]  Brian A. Bucci,et al.  Performance of artificial neural network-based classifiers to identify military impulse noise. , 2007, The Journal of the Acoustical Society of America.

[10]  Oleksiy B. Pogrebnyak,et al.  Noise Pattern Recognition of Airplanes Taking Off: Task for a Monitoring System , 2007, CIARP.

[11]  C. Stewart,et al.  Detection and classification of acoustic signals from fixed-wing aircraft , 1991, IEEE 1991 International Conference on Systems Engineering.

[12]  Ignazio Dimino,et al.  Experimental Training and Validation of a System for Aircraft Acoustic Signature Identification , 2007 .

[13]  C.-C. Jay Kuo,et al.  Audio content analysis for online audiovisual data segmentation and classification , 2001, IEEE Trans. Speech Audio Process..

[14]  H. Bolandi,et al.  Air Target Classification in Two Dimensional Feature Space , 2006, 2006 IEEE International Conference on Industrial Technology.

[15]  Luis Pastor Sánchez Fernández,et al.  Aircraft Classification and Acoustic Impact Estimation Based on Real-Time Take-off Noise Measurements , 2013, Neural Processing Letters.

[16]  Evangelos Dermatas,et al.  Crack detection in noisy environment including raining conditions , 2007 .

[17]  Alexander Sutin,et al.  Acoustic detection, tracking and classification of Low Flying Aircraft , 2013, 2013 IEEE International Conference on Technologies for Homeland Security (HST).

[18]  Douglas Keislar,et al.  Content-Based Classification, Search, and Retrieval of Audio , 1996, IEEE Multim..