| 基于改进的RSSI的车间移动节点MCL算法 |
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| 引用本文： | 刘小园.基于改进的RSSI的车间移动节点MCL算法[J].吉首大学学报(自然科学版),2018,39(3):20. |
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| 作者姓名： | 刘小园 |
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| 作者单位： | （罗定职业技术学院电子信息系，广东 罗定 527200） |
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| 基金项目： | 广东省科技厅专项项目（2016A020212020）；广东省高等职业技术教育研究会2016年立项课题项目（GDGZ16Y079）；中国职业技术教育学会教学工作委员会2017-2018研究课题（1710570） |
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| 摘 要： | 针对蒙特卡罗定位（MCL）算法在无线传感网络定位精度和取样效率中存在的不足，提出了一种基于接收信号强度指示（RSSI）改进的MCL算法（R-MCL算法），并对车间移动节点进行定位.通过分析车间移动资源的移动规律，引入RSSI模型测距预测，减少取样区域，从而提高了取样效率和定位精度.仿真结果表明，R-MCL算法在定位精度、收敛速度和计算量等方面的性能均有提升.
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Improved MCL Algorithm for Mobile Nodes in Workshop Based on RSSI |
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| LIU Xiaoyuan.Improved MCL Algorithm for Mobile Nodes in Workshop Based on RSSI[J].Journal of Jishou University(Natural Science Edition),2018,39(3):20. |
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| Authors: | LIU Xiaoyuan |
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| Institution: | (Department of Electronic Information System,Luoding Polytechnic,Luoding 527200,Guangdong China) |
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| Abstract: | Aiming at the deficiencies in localization accuracy and sampling efficiency of Monte Carlo wireless sensor networks,an improved Monte Carlo localization algorithm based on RSSI is proposed to locate the mobile nodes in the workshop.By analyzing the moving rules of mobile resources in workshop,RSSI model is introduced to predict range,reduce sampling area,and improve sampling efficiency and positioning accuracy.The simulation results show that the improved Monte Carlo localization algorithm based on RSSI improves the localization accuracy,convergence speed and computational complexity. |
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| Keywords: | mobile node Monte Carlo location algorithm received signal strength indication |
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