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Analyses and tests of three signal processing methods for helicopter identification.

Analyses and tests of three signal processing methods for helicopter identification. Beställ tryckt exemplar Lägg i kundvagnen
Författare: Carlsson Christina, Bengtsson Jan-Peter, Ödman Svante
Ort: Linköping
Sidor: 28
Utgivningsår: 1993
Publiceringsdatum: 1993-01-27
Rapportnummer: (FOA C 30677-8.4, 3.1)
Nyckelord helikopteridentifiering, akustisk signatur, signalbehandling, spektralanalys, filter, AR-parametrisering, neuronnät, back-propagation, HPLF, helicopter identification, acoustic signature, signal processing, spectrum analysis, AR parametrization, neural networks, 330
Sammanfattning En helikopter i krig utgör en stor fara för markstridskrafter. Den kan plötsligt dyka upp bakom kullar, vegetation etc. En helikopter kan flyga så pass lågt att den undgår upptäckt med radar. Det skulle vara en stor fördel att ändå kunna upptäcka helikoptern, t. ex. med hjälp av dess akustiska signatur. Ljud från helikoptrar och icke-helikoptrar har spelats in med mikrofon. Tre signalbehandlingsmetoder för helikopteridentifiering har studerats och jämförts.
Abstract A helicopter in the battle field is a large threat to soldiers and armoured vechicles. It can suddenly appear behind hills, vegetation etc. A helicopter can also fly close to the ground and avoid detection by radar. Therefore it would be a great advantage still being able to recognize a helicopter, e.g. using its characteristic sound. Sound from helicopters and non-helicopters has been recorded with a microphone. Three signal processing methods for helicopter identification have been studied and compared. The physical model uses the characteristic frequencies of the helicopter and it was implemented as an algorithm. In the filter model, an AR (autoregressive) filter that extracts the characteristics of helicopter sound from white noise has been created. The neural network model accepts an arbitrary input signal and the output signal is "helicopter/not helicopter". The neural network uses the back-propagation training algorithm and an AR parametrization of the signals as pre-processing. The best classifier is the neural network model. It does not suffer from inflexiblity like the physical model or the filter model. There is, though, need for further studies of better pre-processing methods.

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