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本文在分析了幅度排序相关技术以后,研究了一种对敏感器灰度量化排序的相关算法。本文选择合适的量化阈值,对敏感器灰度作量化排序,可将敏感器图分块成若干张0—1数码图。进一步用数码图与参考图相关,结果省去了所有的乘法运算。此外,在确定的量化函数条件下,对灰度为高新分布的图象从理论上分析计算出了度量值的均值与方差,从而得出在不同信噪比条件下的检测门限序列。在计算机上对不同相关长度的随机高斯分布的图象进行的模拟表明,门限序列对信噪比从5到1的敏感器图的匹配,都能有效地进行检测。对实际地形图进行模拟的结果是:当敏感器图的信噪比从3降到1时,都获得了正确的匹配。
After analyzing the related techniques of amplitude ordering, this paper studies a correlation algorithm for ranking gray scale of sensors. In this paper, a suitable quantitative threshold is selected to quantify the gray scale of the sensor. The sensor map can be divided into several 0-1 digital images. The digital map is further related to the reference map, eliminating all multiplication. In addition, the mean and variance of the metric values are theoretically calculated and calculated for the high-gray-scale image under the condition of the defined quantification function so as to obtain the detection threshold sequence under different signal-to-noise ratio conditions. Simulations of random Gaussian distribution images of different correlation lengths on a computer show that the threshold sequence can be efficiently detected for matching S / N ratios from 5 to 1. The result of simulating the actual topographic map is that when the signal-to-noise ratio of the sensor map is reduced from 3 to 1, the correct match is obtained.