Abstract

The problem of object perimeter protection by human steps detection using autonomous seismic activity sensors is solved in this paper. The results of the CME (consecutive mean excision) and FCME (forward consecutive mean excision) algorithms to calculate these sensors threshold level are presented. The algorithms are tested on real experimental data. The number of false alarms for CME and FCME algorithms is equal to 23% 10%, respectively, operating with the envelope of seismic signal. The possibilities to reduce the number of false alarms for algorithms are explored. The signal trend neutralization allows us to reduce the probability of false alarms to 16% and 7% for the CME and FCME algorithms, respectively. The signal amplitude normalization in the analysis interval makes it possible to reduce the number of false alarms to a negligible value. The results allow us to choose the algorithm depending on the input data specifics. The CME algorithm with preliminary normalization of the signal amplitude is the most appropriate to solve the problem of human steps detection by autonomous sensor due to less computational complexity.

中文翻译：

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使用 CME 和 FCME 阈值计算算法检测人体步数

摘要

本文解决了使用自主地震活动传感器检测人体步数来保护物体周界的问题。介绍了用于计算这些传感器阈值水平的 CME（连续平均切除）和 FCME（前向连续平均切除）算法的结果。这些算法在真实的实验数据上进行了测试。CME 和 FCME 算法的误报数分别等于 23% 10%，与地震信号的包络一起运行。探讨了减少算法误报数量的可能性。信号趋势中和使我们能够将 CME 和 FCME 算法的误报概率分别降低到 16% 和 7%。分析区间中的信号幅度归一化使得可以将误报的数量减少到可忽略不计的值。结果使我们能够根据输入数据的具体情况选择算法。由于计算复杂度较低，对信号幅度进行初步归一化的 CME 算法最适合解决自主传感器检测人体步数的问题。