Predicting the Terminal Ballistics of Kinetic Energy Projectiles Using Artificial Neural Networks

摘要

The U.S. Army requires the evaluation of new weapon and vehicle systems through the use of experimental testing and vulnerability/lethality modeling & simulation. The current modeling and simulation methods being utilized often require significant amounts of time and subject matter expertise. This means that quick results cannot be provided to address new threats encountered in theatre. Recently, there has been an increased focus on rapid results for modeling and simulation efforts that can also provide accurate results. Accurately modeling the penetration and residual properties of a ballistic threat as it progresses through a target is an extremely important part of determining the effectiveness of the threat against that target. This paper proposes the application of artificial neural networks to the prediction of the terminal ballistics of kinetic energy projectiles. By shifting the computational complexity of the problem to the fitting (regression) phase of the algorithm, the speed of the algorithm during an analysis is improved when compared to other terminal ballistic models for kinetic energy projectiles. An improvement in overall analysis time can also be realized by removing the need for input preparation by a subject matter expert prior to using the algorithm for an analysis.