Classifying launch/impact events of mortar and artillery rounds utilizing DWT-derived features and feedforward neural networks

Feature extraction methods based on the discrete wavelet transform (DWT) and multiresolution analysis are used to develop a robust classification algorithm that reliably discriminates between launch and impact artillery and/or mortar events via acoustic signals produced during detonation. Distinct characteristics are found within the acoustic signatures since impact events emphasize concussive and shrapnel effects, while launch events are similar to explosions, designed to expel and propel an artillery round from a gun. The ensuing signatures 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 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 time-frequency components characteristic of the aforementioned acoustic signatures at ranges exceeding 2km. The resulting decomposition of the acoustic transient signals is used to produce a separable feature space. Highly reliable classification is achieved with a feedforward neural network classifier trained on a sample space derived from the distribution of wavelet coefficients and higher frequency details found within different levels of the multiresolution decomposition. The neural network developed herein provides a capability to classify events (as either launch (LA) or impact (IM)) with an accuracy that exceeds 88%.