无线传感器网络全移动节点定位算法研究

发布时间：2018-03-11 09:22

本文选题：无线传感器网络　切入点：移动节点定位　出处：《东北电力大学》2017年硕士论文　论文类型：学位论文

【摘要】：节点定位是无线传感器网络中的核心技术之一,在目标追踪、事件监测、定位路由、战场定位等诸多领域应用广泛。在无线传感器网络的很多应用中,随机散布在无线传感器网络中的锚节点和未知节点,很多情况下处于移动状态,而目前很多算法考虑传感器动态性不是很全面,使算法在动态状态下表现不佳。目前的移动节点定位算法多基于部分节点静止,部分节点移动,或者节点的移动按照事先规定好的轨迹,这种移动方式均非所有节点随机移动。另一方面,基于接收信号强度指示(RSSI)的定位算法依据信号强度衰减随着距离的变化原理来估计节点间的距离,RSSI定位算法最重要的问题是路径损耗问题,信号衰减因子随环境不同而变化,一般情况下,大多算法将信号衰减因子取典型值来计算,这将导致严重的测距误差,而且在复杂的地理环境中,信号传输不规则性(DOI)也是信号在实际传输中遇到的一个重要问题。针对以上存在的问题,本文通过大量实地测量,将得到的数据进行分析与处理,使用MATLAB拟合出不同环境下的信号衰减因子与参考距离为1m时的路径损耗值,为了使该模型更加符合真实复杂的情况,将DOI引入模型中,结合信号的传输半径、环境的复杂程度,建立了基于信号接收强度和距离的拟合关系模型,在此基础上将RSSI定位算法应用于锚节点和未知节点全部随机移动的网络中。为了能解决当能够与未知节点通信的锚节点数量少于3个而不能定位的问题,本算法根据能够与未知节点通信的锚节点数量,将定位过程分为3种情况:当能够与未知节点通信的锚节点数量等于或者大于3个时,直接使用三边测量法进行定位;当能够与未知节点通信的锚节点数量为2个时,利用未知节点历史时刻的位置信息辅助当前时刻的未知节点定位,即把未知节点历史时刻的位置作为锚节点的位置,速度值作为通信半径对未知节点进行辅助定位;当能够与未知节点通信的锚节点只有1个时,通过灰预测预测未知节点当前时刻的移动方向,然后结合未知节点历史时刻位置信息和速度值来完成节点定位过程。仿真结果表明,与传统RSSI定位算法和基于卡尔曼滤波的RSSI定位算相比,该算法的定位成功率提高了约30%。
[Abstract]:Node location is one of the core technologies in wireless sensor networks. It is widely used in many fields, such as target tracking, event monitoring, location routing, battlefield location, etc. Anchor nodes and unknown nodes scattered randomly in wireless sensor networks are in a moving state in many cases, but many algorithms currently consider the dynamic nature of the sensor is not very comprehensive. Most of the current mobile node localization algorithms are based on the stationary part of the node, the movement of part of the node, or the movement of the node according to the predetermined trajectory. This movement is not random for all nodes. On the other hand, The location algorithm based on received signal intensity indication (RSSI) estimates the distance between nodes according to the principle of the variation of signal intensity attenuation with distance. The most important problem of RSSI location algorithm is the path loss problem, and the signal attenuation factor varies with the environment. In general, most algorithms calculate the signal attenuation factor by taking the typical value, which will lead to serious ranging errors, and in complex geographical environment, The irregularity of signal transmission is also an important problem in actual signal transmission. In view of the above problems, this paper analyzes and processes the obtained data through a large number of field measurements. MATLAB is used to fit the path loss of signal attenuation factor and reference distance of 1 m in different environments. In order to make the model more realistic and complex, the DOI is introduced into the model, which combines the transmission radius of the signal and the complexity of the environment. A fitting relation model based on the signal receiving intensity and distance is established. In order to solve the problem that the number of anchor nodes that can communicate with unknown nodes is less than 3, the problem of unlocalization can be solved by applying RSSI localization algorithm to all random moving networks of anchor nodes and unknown nodes, so as to solve the problem that the number of anchor nodes that can communicate with unknown nodes is less than three. According to the number of anchor nodes that can communicate with unknown nodes, this algorithm divides the localization process into three cases: when the number of anchor nodes that can communicate with unknown nodes is equal to or greater than 3, the method of trilateral measurement is directly used to locate; When the number of anchor nodes that can communicate with unknown nodes is 2, the position information of unknown nodes' historical moment is used to assist the location of unknown nodes at current time, that is, the position of unknown nodes' historical moment is taken as the position of anchor nodes. When only one anchor node can communicate with the unknown node, grey prediction is used to predict the moving direction of the unknown node at the current time. The simulation results show that compared with the traditional RSSI localization algorithm and the RSSI localization algorithm based on Kalman filter, the localization success rate of this algorithm is increased by about 30%.
【学位授予单位】：东北电力大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP212.9;TN929.5

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