无线传感器网络的室内移动节点定位技术研究

发布时间：2018-11-10 22:39

【摘要】：随着科学技术的快速发展,人们对生活质量的追求不断提高,大数据、云计算、物联网、人工智能成为研究的热点,智能化、网络化、无线化已经走进人们生活的方方面面。当今科技正沿着绿色环保、低耗高能、无线智能的方向发展。正是因为这种趋势,使得无线传感器网络(Wireless Sensor Networks,WSN)成为了现代社会热门技术。由于WSN网络具有大规模、自组织、高鲁棒性、低功耗的特点,特别适用于室内定位系统尤其是室内移动节点的定位系统。正是由于高性能、低功耗的要求,所以针对定位算法的先进性研究,成为研究的热点问题。关于这类问题的研究,论文研究主要内容如下:由于传统Voronoi图等传统定位算法在测距技术等方面存在误差,针对此类问题提出一种在多锚节点下的Voronoi图改进定位算法。通过引进Cayley-Menger行列式,达到对距离关系的限制,从而得到关于测距误差的约束方程,利用拉格朗日乘数法对该方程进行求解,同时把修正后的距离信息应用于多锚节点Voronoi图定位算法中,使该算法得到优化。提出一种改进的最小二乘拟合蒙特卡罗(Least Squares Fitting Monte Carlo,LSFMCL)定位算法,该算法能够克服传统蒙特卡罗定位算法由于节点采样效率低导致的定位精度低的缺陷,它采用最小二乘拟合节点运动轨迹,同时对节点位置进行初步预测,优化缩小采样区域并且确定采样区间,而且提出权值概念并利用预测节点的权值信息,计算未知节点的位置。对室内移动节点的定位技术进行硬件平台的设计搭建,然后利用VB开发室内移动节点定位系统的上位机监控系统。
[Abstract]:With the rapid development of science and technology, people's pursuit of quality of life continues to improve. Big data, cloud computing, Internet of things, artificial intelligence has become a hot research, intelligence, networking, wireless has entered into all aspects of people's lives. Nowadays, technology is developing along the direction of environmental protection, low energy consumption and wireless intelligence. Because of this trend, wireless sensor network (Wireless Sensor Networks,WSN) has become a hot technology in modern society. Due to the characteristics of large scale, self-organization, high robustness and low power consumption, WSN network is especially suitable for indoor positioning systems, especially for indoor mobile nodes. Because of the requirement of high performance and low power consumption, the research on the advancement of localization algorithm has become a hot issue. The main contents of this paper are as follows: because of the errors in the traditional localization algorithms such as traditional Voronoi diagrams, an improved localization algorithm based on Voronoi diagrams with multiple anchor nodes is proposed for this kind of problems. By introducing the Cayley-Menger determinant to limit the distance relation, the constraint equation about the ranging error is obtained, and the Lagrange multiplier method is used to solve the equation. At the same time, the modified distance information is applied to the multi-anchor node Voronoi map localization algorithm, so that the algorithm can be optimized. An improved least square fitting Monte Carlo (Least Squares Fitting Monte Carlo,LSFMCL) localization algorithm is proposed, which can overcome the shortcomings of the traditional Monte Carlo localization algorithm, which is caused by the low sampling efficiency of nodes. It uses the least square to fit the moving track of the node, at the same time, it makes a preliminary prediction of the node position, optimizes the sampling area and determines the sampling interval, and puts forward the concept of weight value and uses the weight information of the predicted node. Calculate the location of unknown nodes. The hardware platform of indoor mobile node positioning technology is designed and built, and then the upper computer monitoring system of indoor mobile node positioning system is developed by using VB.
【学位授予单位】：北京建筑大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP212.9;TN929.5

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