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Analysis of the noise equivalent angle of various tilt-estimation algorithms in the presence of Poisson and Gaussian noise

机译:泊松和高斯噪声存在下各种倾斜估计算法的噪声等效角分析

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Abstract: This paper addresses the problem of estimating the noise equivalent angle (NEA) of several tilt estimation algorithms resulting from including zero-mean Poisson and Gaussian noise. The Poisson noise is due to both photon shot noise and the photoelectric conversion. The readout noise of the detector is assumed to be Gaussian. There are no simple means to relate the noise on the detector to the noise in the measurement. More signal increases the signal-to-noise (SNR) and decreases the NEA. The 2D signal density profile strong influences the NEA. Three different profiles will be analyzed: a Gaussian spot, a top hat, and a simulated missile. The analysis will be performed as a function of total signal. The simulation of these three profiles will be based on using a constant base image with added noise. The variation in the tilt estimate is due to the added noise since each frame would be identical otherwise. The variation as a function of total signal is performed by scaling the base image. Experimental data is analyzed to determine the SNR by dividing the mean by the rms. This data was taken at several different intensity levels so that the total counts would change. Finally, the results from the simulations and the experimental data are compared. The dominant noise not simulated is due to scintillation. Currently, it seems that this last noise source dominates both of the sources included in the simulation. !2
机译:摘要:本文解决了由零均值泊松和高斯噪声引起的几种倾斜估计算法的噪声等效角(NEA)估计问题。泊松噪声是由于光子散粒噪声和光电转换引起的。假定检测器的读出噪声为高斯噪声。没有简单的方法可以将检测器上的噪声与测量中的噪声相关联。更多的信号会增加信噪比(SNR),并降低NEA。 2D信号密度分布图强烈影响NEA。将分析三种不同的轮廓:高斯点,高顶礼帽和模拟导弹。将根据总信号进行分析。这三个轮廓的仿真将基于使用具有附加噪声的恒定基础图像。倾斜估计的变化是由于增加的噪声引起的,因为否则每帧将是相同的。通过缩放基本图像来执行作为总信号的函数的变化。通过将平均值除以均方根值来分析实验数据以确定SNR。该数据是在几个不同的强度级别上获取的,因此总计数将发生变化。最后,比较了仿真结果和实验数据。未模拟的主要噪声是由于闪烁引起的。当前,似乎最后一个噪声源主导了仿真中包括的两个源。 !2

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