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Sensor-based validation of synthetic thermal scenes: how close is good enough?

机译:基于传感器的合成热场景验证:多近才足够好?

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Abstract: Using synthetic background scenes in the modeling of thermal infrared sensor-based smart munitions offers tremendous flexibility in exploring the performance envelope of these systems. However, to reach this goal, the synthetic background generation process must undergo the scrutiny of verification and validation to be accredited for use with a specific sensor system. Traditional approaches to validating synthetic scenes range from low-level subjective comparison to absolute pixel-to-pixel agreement between the two scenes. Neither of these approaches considers the specific smart munition sensor and processor which ultimately use the scene. In this paper we present an alternate validation approach based on comparison between end performance of a thermal infrared sensor-based smart munition system using synthetic/real scene pairs. Paired synthetic/real thermal scenes, including a low and a high-clutter level, are compared with conventional validation metrics and with the performance-based metric, using various smart munition sensor targeting algorithms. The degree of scene fidelity (absolute agreement between scene pairs) required to replicate performance varies with clutter level and processor algorithm. Under high clutter conditions, greater synthetic scene fidelity is required to match performance.!14
机译:摘要:在基于热红外传感器的智能弹药的建模中使用合成背景场景可为探索这些系统的性能范围提供极大的灵活性。但是,为了达到这个目标,合成背景生成过程必须经过验证和确认的审查,才能获得与特定传感器系统一起使用的资格。验证合成场景的传统方法范围很广,从低级主观比较到两个场景之间的绝对像素间一致性。这些方法均未考虑最终使用场景的特定智能弹药传感器和处理器。在本文中,我们基于使用合成/真实场景对的基于热红外传感器的智能弹药系统的最终性能之间的比较,提出了一种替代验证方法。使用各种智能弹药传感器瞄准算法,将成对的合成/真实热场景(包括低杂波水平和高杂波水平)与常规验证指标和基于性能的指标进行比较。复制性能所需的场景保真度(场景对之间的绝对一致性)随杂波级别和处理器算法而变化。在高杂波条件下,需要更高的合成场景保真度以匹配性能!14

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