【作者】 姜莉；

【导师】 解永平；

【作者基本信息】 大连理工大学 ， 通信与信息系统， 2010， 硕士

【摘要】 随着无线定位技术的发展,室内定位技术成为人们关注的热点。现有的室内定位技术主要有：光跟踪定位技术、A-GPS定位技术、超声波定位技术、、WiFi技术等。基于WiFi的定位技术具有覆盖范围广,信息传输速度快,实现成本较低等优点倍受人们的关注。本课题来自于大连理工大学与OKI公司联合研究项目——基于WiFi室内定位技术的研究,目的是在室内环境下实现3米的定位精度。由于室内环境下的信号波动很大,导致前期项目的定位系统精度的不高,不能满足3米定位误差内达到75%的要求。为了提高定位精度,可从RSSI (Received Signal Strength Indicator)信号测量、RSSI信号处理、室内传播模型、定位算法等方面进行研究。本文主要对RSSI信号处理、室内传播模型、定位算法这三方面进行研究。首先,本文根据定位系统中的终端状态将RSSI信号处理方法分为静态RSSI信号处理和动态RSSI信号处理。通过实验分析得出RSSI信号在室内的分布特征,根据该特征提出一种静态RSSI信号处理方法——对数模型法；再针对现有的动态RSSI信号处理方法的缺点,提出了修正加权滤波法。其次,在现有的实验条件下对几种典型的室内传播模型进行了仿真比较,得出MK(Motley-Keena)模型较优,并对MK模型做了进一步改进；在分析基于室内传播模型定位算法的基础上,提出了两种最大似然估计法,并对这两种最大似然估计法进行仿真比较。最后,本文在WiFi环境下将所研究的RSSI信号处理、室内传播模型和定位算法运用到定位系统中,得到3.02米定位误差内累积概率达到75%,可以满足项目的要求。更多还原

【Abstract】 With the development of wireless location technology, indoor location has become a hot spot. Indoor location technologies at present mainly include optical tracking, A-GPS, ultrasonic and WiFi, etc. Indoor location technology based on WiFi has many advantages such as wide coverage area, high-speed information transmission, low cost, etc.WiFi is paid more attention by people.This subject comes from the joint research project of Dalian University of Technology and OKI Corporation-WiFi-based indoor positioning,the aim is to achieve 3 meter positioning accuracy under the indoor environment.Because under indoor environment signal fluctuation is very big, causes the positioning system’s accuracy of the preliminary project is not high, cannot satisfy the 3 meter positioning error range to meet 75% requirements.In order to increase the pointing accuracy, can do reseach in following aspects:RSSI (Received Signal Strength Indicator) measurement, RSSI signal processing, indoor propagation models,positioning algorithm.This paper foucuses on three aspects:RSSI signal processing, indoor propagation model and positioning algorithmFirst, According to the state of positioning terminals, we divided the signal processing methods into static signal processing and dynamic signal processing. We manage to discover the distributed characteristics of signal strength in the indoor environment by analyzing the statistic characters of indoor signal strength, and propose one kind of static signal processingthe logarithm model. Then, given the disadvantages of existing dynamic data processing method, we propose a method of corrected outliers and weighted filtering.Second, we make simulations of some typical propagation model and compare their simulation results in current experimental conditions. The Motley-Keenan model is the best. We further improve Motley-Keenan model. Then,in the analysis of exiting location algorithm based on propagation model, we propose two methods of maximum likelihood estimate. We make simulation to compare locating accuracy and performance of these two algorithms.Last, the above signal processing, model and location algorithm are applied to the locating system in a real WiFi environment. The locating results are the distribution of cumulative error is 75% within 3.02-meter locating error. It fulfilled the project requirement.更多还原