空中目标算法开发(ATAD)

B. Overfield, Jason Thomas, M. Cohen, V. Sylvester, R. Rogers, D. Morgan
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引用次数: 0

摘要

战斗空军在传感器和武器能力上投入了大量资金,以探测和攻击远程空中目标。不幸的是,识别这些目标的能力落后于这些能力。在空军赞助的空中目标算法发展(ATAD)计划下,基于模型的推理(MBR)融合算法已经开发并演示了用于改进空中目标识别(ID)的算法。预期的回报是一个健壮的ID算法,它提供了改进的时间表、增加的ID置信度、增强的目标方面性能、对对策的健壮性和更长的ID范围。该技术适用于所有当前和未来的战斗机。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Air target algorithm development (ATAD)
The Combat Air Forces have invested heavily in a sensor and weapon capability to detect and attack air targets at long ranges. Unfortunately, the ability to identify these targets lags behind these capabilities. Under the Air Force sponsored Air Target Algorithm Development (ATAD) program, model-based reasoning (MBR) fusion algorithms have been developed and demonstrated for improved air target identification (ID). The expected payoff is a robust ID algorithm that offers improved timelines, increased ID confidence, enhanced target aspect performance, robustness to countermeasures, and longer ID ranges. The technology is applicable to all current and future fighter aircraft.
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