基于RSSI的室内无源感知模型和定位算法研究

发布时间：2018-01-02 05:34

本文关键词：基于RSSI的室内无源感知模型和定位算法研究　出处：《安徽大学》2017年硕士论文　论文类型：学位论文

【摘要】：现如今,无线传感器网络作为物联网底层感知的重要组成部分得到了越来越多的发展和应用,与此同时,人们对于感知定位技术的需求也越来越高;因此,利用无线传感器网络进行无源感知定位作为一种新的研究方向得到了广泛关注。基于无线传感器网络的无源感知定位方法与传统的感知定位方法相比有着许多优点。常用的通过检测光线、超声波、红外线等物理属性的感知定位方法会受到很多环境因素的限制,如光线、温度、盲区等。基于无线传感器网络的无源感知方法可以不受上述因素的干扰,它能有效的适应各种变化的环境,并且不需要感知目标配合携带任何的电子标签和移动设备,是一种新型的无源感知方法。因此它具有更为广阔的发展和应用前景。在无线传感器网络中使用射频信号进行无源感知的方法,本质上是利用了障碍物遮挡对无线信号链路的影响,当有物体遮挡无线链路时,会使得链路信号接收强度(Received Signal Strength Indication,RSSI)发生改变,通过分析无线链路的这种变化属性,总结其变化规律,可以实现在实验区域内进行人员感知的目的,并通过利用椭圆模型算法,对感知区域内存在的人员进行定位。因此,本文围绕室内无源感知模型和定位算法开展研究。论文首先分析了室内无线信号的传播特征和衰落模型,分析找出障碍物对信号功率的影响原因,并总结了椭圆模型定位方法,在此基础上设计了无源感知定位实验模型和实验方案,这其中包括了对感知定位流程的规划,对实验平台的设计,所采用的数据收集和处理方法以及感知定位所采用的算法程序。同时,在实验模型的基础上研究了椭圆模型定位算法,并根据实验结果分析算法的缺陷,对定位算法进行了改进,解决了定位中出现的边缘效应,提高椭圆模型定位算法的定位精度。在搭建实验系统的硬件选择上,实验中使用了以CC2530芯片作为控制核心的ZigBee无线传感器网络定位节点硬件模块;在软件编程中,实验中通过IAR Embedded Workbench编译环境使用了 TI官方提供的简单协议Basic RF来编写和修改节点程序,完成感知节点的程序设计;通过使用环协机制实现了对于整个传感器网络节点的控制和数据采集,完成了无源感知定位实验模型的软硬件平台搭建,并对实验结果进行分析。
[Abstract]:Nowadays, as an important part of the bottom perception of the Internet of things, wireless sensor networks have been more and more developed and applied. So... Passive sensing localization based on wireless sensor network (WSN) has been paid more attention as a new research direction. Compared with traditional sensor network, passive sensing localization method based on wireless sensor network has many advantages. Point. Commonly used by detecting light. Ultrasonic, infrared and other physical properties of the sensing and positioning methods will be limited by many environmental factors, such as light, temperature. Blind area, etc. The passive sensing method based on wireless sensor network can not be disturbed by the above factors, it can effectively adapt to all kinds of changing environment. And does not need to sense the target to carry any electronic tags and mobile devices. It is a new passive sensing method. Therefore, it has a broader development and application prospects. In wireless sensor networks, radio frequency signals are used for passive sensing. In essence, the influence of obstacle occlusion on wireless signal link is utilized, when there is an object blocking wireless link. The received Signal Strength indication (RSSI) of the link signal is changed. By analyzing the changing attributes of wireless link and summarizing its changing rules, we can realize the purpose of personnel perception in the experimental area, and use the elliptic model algorithm. Therefore, this paper focuses on the indoor passive sensing model and localization algorithm. Firstly, the propagation characteristics and fading model of indoor wireless signal are analyzed. The influence of obstacles on signal power is analyzed and the elliptic model localization method is summarized. Based on this the passive sensing localization experimental model and experimental scheme are designed. This includes the planning of the perceptual localization process, the design of the experimental platform, the data collection and processing methods used and the algorithm program used in the perceptual localization. At the same time. Based on the experimental model, the elliptic model localization algorithm is studied. According to the experimental results, the defect of the algorithm is analyzed, and the localization algorithm is improved to solve the edge effect. Improve the location accuracy of the elliptic model location algorithm in building the hardware of the experimental system selection. In the experiment, the hardware module of positioning node in ZigBee wireless sensor network based on CC2530 chip is used. In software programming. In the experiment, we use the simple protocol Basic RF provided by TI to write and modify the node program through IAR Embedded Workbench compilation environment. Complete the program design of the perceptual node; The control and data acquisition of the whole sensor network node is realized by using the mechanism of environment cooperation, and the hardware and software platform of the passive sensing localization experimental model is built, and the experimental results are analyzed.
【学位授予单位】：安徽大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN929.5;TP212.9

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