Discriminating mortar launch/impact events utilizing acoustic sensors

Abstract

Feature extraction methods based on the discrete wavelet transform and multiresolution analysis facilitate the development of a robust classification algorithm that reliably discriminates between launch and impact mortar events via acoustic signals produced during these events. Distinct characteristics arise within the different explosive events because impact events emphasize concussive and shrapnel effects, while launch events result from explosion that expel and propel a mortar round from a gun. The ensuing blast waves are readily characterized by variations in the corresponding peak pressure and rise time of the waveform, differences in the ratio of positive pressure amplitude to the negative amplitude, variations in the prominent frequencies associated with the varying blast events and variations in the overall duration of the resulting waveform. Unique attributes can also be identified that depend upon the properties of the gun tube, projectile speed at the muzzle, and the explosive/concussive properties associated with the events. In this work, the discrete wavelet transform is used to extract the predominant components of these characteristics from the acoustic signatures of the event at ranges of 1km. Highly reliable discrimination is achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of wavelet coefficients and higher frequency details found within different levels of the multiresolution decomposition. We show that the algorithms provide a reliable discrimination (>84%) between launch and impact events using data collecting during several separate field test experiments.