| 陈媛媛,李贤阳.基于综合权值校准与均跳估测的
移动物联网终端坐标感知算法[J].电子测量与仪器学报,2020,34(1):43-50 |
| 基于综合权值校准与均跳估测的
移动物联网终端坐标感知算法 |
| Coordinate sensing algorithm for mobile internet of things terminal based oncomprehensive weight calibration and mean jump estimation |
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| DOI： |
| 中文关键词: 移动物联网 终端坐标感知 权值微分修正 节点乖离 三角定位 平均邻域半径 均跳估测 |
| 英文关键词:mobile internet of things terminal coordinate sensing weight differential correction node deviation triangular location average neighborhood radius average jump estimation |
| 基金项目:国家自然科学基金资助项目(61273328)、广西高校中青年教师科研基础能力提升项目(2017KY0795)、钦州市科技攻关项目(20164410)、钦州市物联网先进技术重点实验室开放课题(IOT2017B001)项目资助 |
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| 中文摘要: |
| 为提高移动物联网感知终端的定位能力，降低因信道噪声而导致的乖离现象，提出了基于综合权值校准与均跳估测的移动物联网终端坐标感知算法。首先，将定位数据报文拆分为定位节点坐标、跳数、定位节点ID三部分，待定位节点通过解析定位数据报文，获取与定位节点间的跳数及坐标，当仅当待定位节点同时接收到多个定位数据报文时，从中解析定位节点坐标，并根据接收到的3个定位节点坐标来构建三角定位方法，以精确获取坐标，降低坐标抖动现象。随后，根据均方差理论来设计了基于权值微分修正的邻域半径预定位方法，实现定位坐标的精确化获取，引入微分方式增强定位精度。最后，根据不同定位节点在网络中的权值差异，设计了基于均跳估测的终端坐标精确化方案，采取均衡化机制来增强网络对节点的捕捉能力，以修正节点坐标，从而提高物联网终端感知精度。仿真实验表明，与当前相关的物联网节点定位技术相比，该算法具有更低的节点坐标乖离程度和更强的运动轨迹预测能力。 |
| 英文摘要: |
| In order to improve the positioning ability of mobile Internet of Things sensing terminals and reduce the deviation caused by channel noise, this paper proposes a coordinate sensing algorithm for mobile Internet of Things terminal based on comprehensive weight calibration and mean jump estimation. Firstly, the localization data message is divided into three parts: coordinates, hops and ID of the localization node. The localization node parses the localization data message and obtains the hops and coordinates between the localization nodes. When only the localization node receives multiple localization data messages at the same time, the localization data message is divided into three parts. The coordinates of the positioning nodes are analyzed, and a triangular positioning method is constructed according to the coordinates of the received three positioning nodes. The coordinates are obtained accurately and the coordinate jitter is reduced. Subsequently, according to the mean square error theory, a neighborhood radius pre positioning method based on weight differential correction is designed to accurately obtain the positioning coordinates. Finally, according to the weight difference of different locating nodes in the network, a terminal coordinate correction scheme based on average hop estimation is designed. The equalization mechanism is adopted to enhance the network’s ability to capture nodes, and the inductive processing method is used to optimize the locating equations and modify the node coordinates, so as to improve the terminal sensing accuracy of the Internet of Things. The simulation results show that the proposed algorithm has lower deviation degree of node coordinates and better trajectory prediction ability than the current Internet of things node positioning technology. |
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