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Modeling of aliasing effects for point target detection in undersampled IR imaging systems

机译:光点瞄准射出点目标检测的锯齿效应建模

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This paper presents a Fourier transform model of the aliasing effects caused by two-dimensional sensor undersampling. The SNR probability distribution at the sensor output is numerically generated and compared with simulation results. This distribution is needed for estimating the track score gain when SNR is used as a track feature. Modeling of aliasing makes it possible to calculate the sensor mean signal to noise ratio (SNR), the sensor radiometric measurement precision (RMP) and the sensor position measurement precision (PMP) without the use of Monte Carlo simulations. An example of a sensor design trade is presented in which the detector size is maximized with respect to ROC performance.
机译:本文介绍了由二维传感器欠采样引起的锯齿效应的傅里叶变换模型。传感器输出处的SNR概率分布在数值上产生并与仿真结果进行比较。当SNR用作轨道特征时,需要该分布需要估计轨道分数增益。别名建模使得可以计算传感器平均信号到噪声比(SNR),传感器辐射测量精度(RMP)和传感器位置测量精度(PMP)而不使用Monte Carlo仿真。介绍了传感器设计交易的示例,其中检测器大小相对于ROC性能最大化。

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