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Ground-target infrared signature uncertainties and their effect on simulation and validation

机译:地面目标红外签名的不确定性及其对模拟和验证的影响

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Data and analyses demonstrating the variability and uncertainties in infrared (IR) ground target signatures are presented. The uncertainties are due to a variety of factors ranging from environmental effects to differences in vehicle configurations. Caution must be exercised when using predictive models for simulations because these models are usually pristine and present repeatable signatures for a given set of inputs. Actual vehicle signatures show the effects of wear and tear, aging, poor maintenance, etc., and these effects will vary from vehicle to vehicle. Vehicles encountered in real-life often have a variety of crew- specific signature components that will affect the signature of a vehicle as well, such as stowage of supplies or spare parts. It is important therefore to develop the concept of a `representative' target and take into consideration the expected variations from a baseline signature. It is common in an infrared scene simulation to have a single signature for a given type of vehicle when more than one of the vehicles is present in the scene at the same time. Limiting the target data in this manner can lead to biased results as observers and algorithms can memorize a particular signature. To avoid this, ground target signatures used for training simulations and algorithm development should incorporate variability in the target signatures. Varying ground target signatures in this manner will provide for more realistic sensor performance assessment, training, and algorithm development. The primary signature factor affecting training and algorithm development will be vehicle configuration. Model developers often use temperature deltas when assessing the fidelity of an IR signature model. When validating an infrared signature model, whether it is digital or a target surrogate, the model developer should take into account the uncertainty in the target signature caused by measurement errors and target surface optical variations. Portions of a vehicle where paint has been removed have greatly reduced emissivity and often the reflected radiance will be from the sky. This can lead to temperature errors of tens of degrees Celsius. As paint ages or get dirty its optical characteristics change which can also cause infrared signature variations. All of these surface factors (and more) lead to a general uncertainty in the IR signature of a ground vehicle.
机译:展示了数据和分析,展示了红外线(IR)地面目标签名中的变异性和不确定性。不确定性是由于各种因素从环境影响到车辆配置的差异。在使用预测模型时,必须小心谨慎行使,因为这些模型通常是原始的,并且给定集合输入的可重复签名。实际的车辆签名显示磨损和撕裂,老化,维护差等的影响,这些效果将因车辆而异。现实生活中遇到的车辆通常具有各种特定的签名组件,这些组件也会影响车辆的签名,例如供应或备件的用途。因此,重要的是要制定“代表性”目标的概念,并考虑到基线签名的预期变化。在红外场景模拟中常见于当在场景中存在的多个车辆在场景中存在多于一个车辆时,具有单个签名。以这种方式限制目标数据可能导致偏置结果,因为观察者和算法可以记住特定签名。为避免这种情况,用于训练模拟和算法开发的地面目标签名应包括目标签名中的可变性。以这种方式变化的地面目标签名将提供更现实的传感器性能评估,培训和算法开发。影响训练和算法开发的主要签名因素将是车辆配置。模型开发人员通常在评估IR签名模型的保真度时使用温度Δ。验证红外签名模型时,无论是数字还是目标代理,模型开发人员都应考虑由测量误差和目标表面光学变化引起的目标签名中的不确定性。除了涂料被移除的车辆的部分大大降低了发射率,并且往往将来自天空的反射光线。这可能导致几十摄氏度的温度误差。作为油漆年龄或脏的光学特性变化,也可能导致红外签名变化。所有这些表面因子(和更多)导致地面车辆的IR标志中的一般不确定性。

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