Neural network classification of air and land vehicle acoustic signatures

Abstract

The temporal and spectral features of the acoustic signatures of aircraft and highway vehicles are used to train two types of neural networks. The detection and classification performances of the networks are then evaluated using an independent set of data. The types of networks evaluated are (i) feedforward with backpropagation training, and (ii) probabilistic nets with maximum-likelihood training. The results demonstrate accurate classification as to type of vehicle during aircraft takeoff or under load conditions for heavy trucks, depending on the signal-to-noise ratio. Neural nets appear to offer a promising vehicle classifier for monitoring airport and highway traffic for compliance with noise regulations.

Military aircrafts’ classification based on their sound signature

“…Research work on aircraft acoustic classification is mainly developed for noise monitoring and for airport traffic monitoring purposes (Asensio et al, 2010;Brooks and DeMetz, 1996;Fernandez et al, 2007). As mentioned, both military and commercial aircrafts can be classified using radars or according to recent advances in research, based on their motions' characteristics (Golmohammad et al, 2006) and on the communication signals they transmit (Feng et al, 2008).…”