基于信号强度差异的移动节点定位算法研究

发布时间：2018-06-13 11:06

本文选题：移动无线传感器网络 + 中垂线法　；参考：《中国矿业大学》2017年硕士论文

【摘要】：移动节点定位问题是无线传感器网络的研究热点之一,它利用少量位置已知节点,按照某种机制对未知节点进行定位。现有算法存在仅适用于静止节点、测距方法受环境影响大、定位精度低等问题,随着无线传感器网络技术的进步和完善,越来越多场合需要定位技术的支撑,因此对移动无线传感器网络技术的研究有重要理论意义和应用价值。现有移动节点定位算法充分利用节点的移动性减小定位复杂度和硬件代价,可是忽略了一些潜在定位信息,滤波条件少,导致定位时间长、定位精度低。为了解决这个问题,本文提出一种基于信号强度差异的移动节点定位算法(NMCL),充分提取潜在的可能位置信息,利用中垂线法和近似三角形内点测法对区域进行划分,缩小未知节点可能存在的位置区域,从而提高定位精度。此外,针对NMCL算法中始终以未知节点移动最大速度作为固定的采样半径,导致预测阶段采样面积过大,定位精度低的问题,本文还提出一种基于信号强度差异的速度自适应改进算法(INMCL),根据这一时刻和上一时刻未知节点接收到的信号强度差异,计算未知节点单位时间内移动的特征距离,再将特征距离转化为物理距离,作为预测时采样区域的半径,从而根据节点移动速度实时变化采样面积,提高定位精度。大量的仿真模拟表明,NMCL算法和INMCL算法较现有移动算法在定位速度较大、锚节点密度较低、通信不规则度强等情况下,定位精度有很大的提高。
[Abstract]:Mobile node localization is one of the research hotspots in wireless sensor networks. It uses a small number of known nodes to locate unknown nodes according to some mechanism. The existing algorithms are only applicable to stationary nodes, ranging methods are greatly affected by the environment, low positioning accuracy and other problems. With the progress and improvement of wireless sensor network technology, more and more occasions need the support of location technology. Therefore, the research of mobile wireless sensor network technology has important theoretical significance and application value. The existing mobile node localization algorithms make full use of the mobility of the nodes to reduce the localization complexity and hardware cost, but ignore some potential location information, so the filtering conditions are less, resulting in long localization time and low positioning accuracy. In order to solve this problem, a mobile node location algorithm based on signal intensity difference is proposed in this paper, which can fully extract the potential position information, and divide the region by using the method of mid-vertical line and the method of approximate triangle point measurement. The location of unknown nodes may be reduced to improve the positioning accuracy. In addition, in the NMCL algorithm, the maximum velocity of unknown nodes is always taken as the fixed sampling radius, which leads to the problem that the sampling area is too large and the positioning accuracy is low in the prediction stage. This paper also presents an improved speed adaptive algorithm based on signal strength difference. According to the difference of signal strength between the unknown nodes at this time and the previous moment, the characteristic distance of the unknown node moving in unit time is calculated. Then the feature distance is transformed into physical distance, which is the radius of the sampling area during prediction, and the sampling area is changed in real time according to the moving speed of the node, and the positioning accuracy is improved. A large number of simulations show that the localization accuracy of NMCL algorithm and INMCL algorithm is much higher than that of the existing algorithms under the conditions of higher localization speed, lower anchor node density and stronger communication irregularity.
【学位授予单位】：中国矿业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP212.9;TN929.5

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