Helicopter detection and classification demonstrator

A technology demonstrator that detects and classifies different helicopter types automatically, was developed at TNO-FEL. The demonstrator is based on a PC, which receives its acoustic input from an all-weather microphone. The demonstrator uses commercial off-the-shelf hardware to digitize the acoustic signal. The user-interface and the signal processing software are written in Matlab. The demonstrator detects the noise from helicopters; the classification is performed using a database with helicopter-specific features. The demonstrator currently contains information of 11 different helicopter types, but can easily be expanded to include additional types of helicopters. The input signal is analyzed in real time, the result is a classification ranging from "no target" to "helicopter type x", e.g. Lynx Mk2. If the helicopter is classified, its relative speed is estimated as well. The algorithm was developed and tested using a database of different helicopters (hovering and moving) recorded at distances ranging from 90 meter up to 8 kilometer. The sensitivity to noise was investigated using jet, tank, artillery and environmental (wind and turbulence) noise as input. Recently (May-June 1999) the demonstrator was tested during two field trials. Different types of helicopters were detected and classified, at various speeds, heights and distances. The detection range of the demonstrator is adjustable. However, when the range is set to approximately one kilometer, the demonstrator will obtain a high correct classification score, with very few false alarms. The research is funded by the School of Military Intelligence of the Royal Netherlands Army. The objective is to develop a prototype of a helicopter detection and classification groundsensor, which can be used by the Royal Netherlands Armed forces for intelligence gathering of helicopter movements. Future research plans include the acoustic detection and classification of different types of fixed wing aircraft and UAV's.