Liang Zhiqiang, Wei Jianming, Zhao Junyu, Liu Haitao, Li Baoqing, Shen Jie, Zheng Chunlei
Shanghai Institute of Micro-system and Information Technology, Chinese Academy of Sciences, 200050, Shanghai, P.R. China.
Sensors (Basel). 2008 Aug 27;8(8):5106-5119. doi: 10.3390/s8085106.
This paper presents a new algorithm making use of kurtosis, which is a statistical parameter, to distinguish the seismic signal generated by a person's footsteps from other signals. It is adaptive to any environment and needs no machine study or training. As persons or other targets moving on the ground generate continuous signals in the form of seismic waves, we can separate different targets based on the seismic waves they generate. The parameter of kurtosis is sensitive to impulsive signals, so it's much more sensitive to the signal generated by person footsteps than other signals generated by vehicles, winds, noise, etc. The parameter of kurtosis is usually employed in the financial analysis, but rarely used in other fields. In this paper, we make use of kurtosis to distinguish person from other targets based on its different sensitivity to different signals. Simulation and application results show that this algorithm is very effective in distinguishing person from other targets.
本文提出了一种利用峰度(一种统计参数)的新算法,以区分由人行走产生的地震信号与其他信号。该算法适用于任何环境,无需机器学习或训练。由于人或其他在地面上移动的目标会以地震波的形式产生连续信号,我们可以根据它们产生的地震波来分离不同的目标。峰度参数对脉冲信号敏感,因此它对人行走产生的信号比对车辆、风、噪音等产生的其他信号更为敏感。峰度参数通常用于金融分析,但在其他领域很少使用。在本文中,我们利用峰度对不同信号的不同敏感性来区分人与其他目标。仿真和应用结果表明,该算法在区分人与其他目标方面非常有效。