井下无线传感器网络比例差分修正的RSSI节点定位算法

发布时间：2018-08-20 16:47

【摘要】：矿井下对人员的定位在矿井的安全生产中是极其核心的一部分。无线传感器网络技术近些年来作为一类新兴的对信息感知、处理和交互技术,将会为井下安监系统带来飞速的发展和进步。井下安监系统最核心的问题是井下节点的定位问题,没有定位技术井下安全系统的信息采集就失去了其根本的价值。节点定位技术在无线传感器网络中占据着最基础,最核心的部分。在矿井下的特殊环境中,传统的GPS定位方法因为需要接收卫星信号而无法满足在井下环境中的定位监测。目前,井下的定位系统主要采用射频识别的方式,即在人靠近射频节点时才能被检测到,进而得到定位信息,但是射频识别的定位方式只是一种被动的监测方式,无法实现对井下人员的实时定位反馈,针对现在井下安监系统不能实时定位、定位精度差、性能不稳定的问题,结合无线传感器网络知识和井下环境中无线传感器网络的定位现状,提出了两种新型井下无线传感器网络节点定位方法,并且对两种方法进行仿真验证。本文在基于RSSI的多边定位算法的基础上,利用锚节点之间的相互关系获取比例差分系数,将这个系数应用在通过RSSI方法测量得到的节点之间的距离上。RSSI测量方法在不同环境下的测量需要不同的传播模型,且RSSI测距方法由于自身的限制条件,即RSSI对距离近的目标测距精度要远远好于距离远的目标。目标未知节点首先读取在通信范围内的信标节点广播的信息,得到RSSI强度值,通过卡尔曼滤波的方法对RSSI信号除去信号中的噪声,除去噪声的接收信号强度值可以获得更加精确的距离值,继而利用最靠近目标节点的锚节点和其余锚节点构建差分模型,获取系统差分误差,在对目标节点测距时去掉系统差分误差,得到更加精确的距离值;然后利用比例差分的方法继续修正RSSI测距,经过仿真实验,得到定位效果明显好于传统RSSI定位算法精度。针对原始的加权质心定位不够精确,使用了改进的加权质心的方法对井下巷道内的节点进行定位。分析了在巷道环境下节点分布时存在的问题,提出解决方案。在此基础上,进一步采取比例差分的方法对获取到的距离修正,进一步优化加权系数,使节点的定位精度更加逼近真实效果。
[Abstract]:The positioning of personnel under the mine is an extremely important part of the safety production of the mine. Wireless sensor network (WSN) technology, as a new technology of information perception, processing and interaction, will bring rapid development and progress for underground safety monitoring system in recent years. The core problem of downhole safety monitoring system is the location of underground nodes. Without positioning technology, the information collection of downhole safety system will lose its fundamental value. Node location technology occupies the most basic and core part in wireless sensor networks. In the special environment under the mine, the traditional GPS positioning method can not meet the need to receive satellite signals and can not meet the location monitoring in the underground environment. At present, the downhole positioning system mainly adopts the method of radio frequency identification, that is, it can only be detected when people are near the radio frequency node, and then the location information can be obtained. However, the location mode of radio frequency identification is only a passive monitoring method. Can not realize the real-time positioning feedback to the downhole personnel, aiming at the problem that the downhole safety monitoring system can not locate in real time, the positioning accuracy is poor, the performance is unstable, Combined with the knowledge of wireless sensor network and the status quo of wireless sensor network location in underground environment, two new underground wireless sensor network node localization methods are proposed, and the two methods are simulated and verified. In this paper, based on the multilateral localization algorithm based on RSSI, the proportional difference coefficient is obtained by using the relationship between anchor nodes. This coefficient is applied to the distance between nodes measured by RSSI method. The measurement of RSSI in different environments requires different propagation models, and the RSSI ranging method is limited by its own conditions. That is, the ranging accuracy of RSSI is much better than that of long range target. The target unknown node first reads the information broadcast by the beacon node in the communication range, obtains the RSSI intensity value, and removes the noise from the RSSI signal by Kalman filter. The received signal intensity value without noise can obtain more accurate distance value, and then use the anchor node closest to the target node and the other anchor nodes to construct a differential model to obtain the differential error of the system. The system difference error is removed and the range value is more accurate. Then the method of proportional difference is used to continue to modify the RSSI ranging. The simulation results show that the accuracy of the localization algorithm is better than that of the traditional RSSI localization algorithm. Because the original weighted centroid location is not accurate, the improved weighted centroid method is used to locate the nodes in the underground roadway. The problems of node distribution in roadway environment are analyzed, and the solutions are put forward. On this basis, the method of proportional difference is adopted to correct the distance obtained, and the weighting coefficient is further optimized, so that the positioning accuracy of the node is closer to the real effect.
【学位授予单位】：江西理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN929.5;TP212.9

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