An application is proposed for detection and classification of battlefield ground vehicles using the emitted acoustic
signal captured at individual sensor nodes of an ad hoc Wireless Sensor Network (WSN). We make use of the
harmonic characteristics of the acoustic emissions of battlefield vehicles, in reducing both the computations
carried on the sensor node and the transmitted data to the fusion center for reliable and effcient classification of
targets. Previous approaches focus on the lower frequency band of the acoustic emissions up to 500Hz; however,
we show in the proposed application how effcient discrimination between battlefield vehicles is performed using
features extracted from higher frequency bands (50 - 1500Hz). The application shows that selective time domain
acoustic features surpass equivalent spectral features. Collaborative signal processing is utilized, such that
estimation of certain signal model parameters is carried by the sensor node, in order to reduce the communication
between the sensor node and the fusion center, while the remaining model parameters are estimated at the fusion
center. The transmitted data from the sensor node to the fusion center ranges from 1 ~ 5% of the sampled
acoustic signal at the node. A variety of classification schemes were examined, such as maximum likelihood,
vector quantization and artificial neural networks. Evaluation of the proposed application, through processing of
an acoustic data set with comparison to previous results, shows that the improvement is not only in the number
of computations but also in the detection and false alarm rate as well.
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