Acoustic landmine detection (ALD) is a technique for the detection of buried landmines including non-metal mines. Since it gives complementary results with GPR or metal detection, sensor fusion of these techniques with acoustic detection would give promising results. Two methods are used for the acoustic excitation of the soil: laser excitation and loudspeaker excitation. A promising concept is using lasers for excitation and monitoring for complete stand-off detection. Results from a field test and laboratory experiments show the feasibility of laser excitation for ALD. In these experiments buried landmine surrogates were measured with ALD using a Nd-YAG laser at 1.06 μm for the acoustic generation and a Laser Doppler Vibrometer (LDV) system at 1.54 μm for the detection of soil vibrations. An analysis is given of the experimental results showing the potential and the inherent limitations of the technique. We discuss the relative merits of LDV detection versus microphone detection of the laser-induced acoustic vibration. It was found that the LDV has limitations with respect to microphone detection due to the influence of surface effects that are prominent in LDV but absent in microphone detection.
Measurements of different types of aircraft are performed and used to obtain information on target characteristics and develop an algorithm to perform classification between jet aircraft, propeller aircraft and helicopters. To obtain a larger detection range, reduce background noise and to reduce classification errors in a multi-target environment, a real time adaptive beamformer algorithm is developed for a three microphone array. The output of the beamformer is submitted to a tracking algorithm. Acoustic signals from identified tracks are submitted to the classification algorithms. The algorithm is tested on data recorded during various field trials. The objective of the research, which is part of a research program for the Dutch Army, is to detect the passage of an aircraft with one or more mechanical wave sensors, either acoustic or seismic. After detection of a target, classification of the type of aircraft is requested (for example: helicopter-jet-propeller-rpv). If possible type identification is also requested. Earlier work showed promising results for detection and classification of helicopter targets. The projects resulted in an algorithm that can detect and classify helicopters, but it was developed to reject other targets. The chosen approach is to combine new aircraft detection and beamforming algorithms with the existing algorithms.
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 MatLabTM. 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.
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