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Improved detection of particle sources with a cubic directional array using a mean test

机译:利用平均试验改善了立方定向阵列的粒子源的检测

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Detection of particles from a far-field source is important in many applications, including optical communications, nuclear hazard detection, radioactivity and detection of cosmic particles. In this paper, we revisit the problem of detection of far-field particle sources using a cubical array. For the case when the source location is deterministic, assuming Poisson arrival of particles, we propose a mean test (MT) and present an asymptotic analysis for the probabilities of false-alarm and signal detection. Through extensive Monte Carlo simulations, we show that this novel and simple test outperforms the existing techniques such as the mean-difference test, generalized likelihood ratio test, the source intensity test and a linear-quadratic difference test. Next, we study the performance of MT in the case when the source location is random - in which case the other techniques cannot be employed - and highlight its utility. In all cases, we validate our analysis through Monte Carlo simulations.
机译:从远场源检测粒子在许多应用中是重要的,包括光通信,核危害检测,放射性和宇宙粒子的检测。在本文中,我们通过立方体阵列重新审视检测远场粒子源的问题。对于源位置是确定性的情况,假设泊松到达粒子的到达时,我们提出了一个平均测试(MT)并呈现了假警报和信号检测的概率的渐变分析。通过广泛的Monte Carlo模拟,我们表明,这部新颖简单的测试优于现有技术,如平均差异测试,广义似然比测试,源强度测试和线性二次差异测试。接下来,我们在源位置随机时研究MT的性能 - 在这种情况下,不能采用其他技术 - 并突出显示其实用程序。在所有情况下,我们通过Monte Carlo模拟验证了我们的分析。

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