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

Sachi Desai; Myron Hohil; Amir Morcos

doi:10.1117/12.667877

17 April 2006 Classifying launch/impact events of mortar and artillery rounds utilizing DWT-derived features and feedforward neural networks

Sachi Desai, Myron Hohil, Amir Morcos

Author Affiliations +

Proceedings Volume 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV; 62470R (2006) https://doi.org/10.1117/12.667877
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

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%.

Citation Download Citation

Sachi Desai, Myron Hohil, and Amir Morcos "Classifying launch/impact events of mortar and artillery rounds utilizing DWT-derived features and feedforward neural networks", Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470R (17 April 2006); https://doi.org/10.1117/12.667877

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available