A Virtual Reality Approach to the Assessment of Damage Effectiveness of Naval Artillery Ammunition Against Unmanned Surface Vessels

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Abstract

Conventional evaluations of naval artillery's destructive prowess are dependent on resource-intensive sea trials, which are neither economically viable nor advantageous for the cultivation of combat intelligence. To circumvent these limitations, we propose a method rooted in virtual reality for assessing the destructive efficacy of naval artillery ammunition against unmanned maritime targets. Leveraging the Unity3D engine, we construct a high-fidelity virtual testing environment and design corresponding testing tasks. The offensive strategy is devised considering the naval artillery's damage radius and range, incorporating factors such as the warhead's velocity, and determining its launch angle and direction. Following this, the projectile's trajectory is computed employing the point mass motion equation, facilitating the simulation of the projectile's entire life cycle trajectory. A Monte Carlo based model has been developed for assessing the damage effectiveness of naval artillery ammunition against unmanned boats. This model enables quantitative calculation of ammunition usage under varied damage probabilities, addressing issues related to the complexity of the testing process, the limited number of tests, and the challenges in computing damage probabilities for target impacts of naval artillery ammunition. The findings corroborate the precision and stability of the virtual reality based assessment method for naval artillery ammunition damage effectiveness against unmanned surface vessels in combat simulations. This verification holds substantial importance for enhancing the computation accuracy of damage effectiveness associated with naval artillery ammunition.

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Yang, W., Zhong, W., Zhang, L., & Jiang, Y. (2023). A Virtual Reality Approach to the Assessment of Damage Effectiveness of Naval Artillery Ammunition Against Unmanned Surface Vessels. IEEE Access, 11, 93500–93510. https://doi.org/10.1109/ACCESS.2023.3310214

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