基于蓝牙4.0基站角度测量的室内定位技术研究

发布时间：2018-01-15 14:19

本文关键词：基于蓝牙4.0基站角度测量的室内定位技术研究　出处：《浙江大学》2016年硕士论文　论文类型：学位论文

【摘要】：随着现代社会科技的发展和互联网技术的发展,室外定位技术也越来越成熟,谷歌地图、百度地图、高德地图的被大面积使用,人类从室外定位技术中获取到的方便也越来越多。受助于成熟的室外定位技术,人们可以很方便地查询到附近的各类商店服务,同时也可以很便捷地获取到各地的最佳路线,甚至公交路线、自驾车路线以及步行路线都一目了然。既然室外定位技术能给人类带来这么多的便利,那么,人们的视线开始投向室内定位技术,如何在室内精度定位物体位置成为了急需解决的问题。本文从大量实验入手,采集足够多的定位数据,通过函数拟合、神经网络和滤波预测的训练处理,提出了通过测量信号强度,训练出物体相对于信号基站的夹角,然后通过三角定位求出物体坐标,最后对位置坐标进行滤波预测的一整套定位方案。整个方法的具体工作如下：一、分别进行室外理想环境下定位实验、室内理想环境下定位实验和室内复杂环境下的定位实验。在室内外的理想环境下,本文使用函数拟合的方法进行定位分析,发现该环境下的角度平局定位精度达到5。,同时定位准确率达到70%。在室内复杂环境下时,函数拟合方法不再适用,于是本文采用了神经网络方法对数据进行训练,将180cm精度上的定位准确率提高到65%。二、为了进一步提高定位精度与定位准确率,本文在神经网络基础上运用滤波预测方法对数据进行修正。主要包括两种方式,其一为坐标线性预测算法,通过线性叠加BP神经网络与基本卡尔曼滤波器,并将神经网络输出向量作为滤波预测中的观测变量,从而实现对输出向量的滤波预测；其二为网络权值滤波算法,使用扩展卡尔曼滤波对神经网络的权值矩阵进行预测修正。两种方法对复杂环境下的定位准确率均有提升,其中坐标线性预测算法表现优秀,180cm精度上的定位准确率提高到70%。三、本文实现了基于双天线蓝牙基站、Android蓝牙信号采集模块与定位呈现模块和后台实现定位算法的服务器端的室内复杂环境定位系统。双天线蓝牙基站是在iBeacon蓝牙基站基础上改进的信号发生器,而Android蓝牙信号采集模块是采集蓝牙信号数据的载体,同时,一个Android移动端其实也代表着一个用户,服务器端则是完成定位数据的关键部分,它将对从Android移动端发来的信号强度数据进行上述分析,计算出定位坐标发送给移动端,而移动端则根据发送回来的位置数据进行实时位置更新。整个系统包含信号数据采集、定位算法实现和定位结果呈现等模块,互相配合,为室内定位研究提供了一种实现方式。
[Abstract]:With the development of modern social science and technology and the development of Internet technology, outdoor positioning technology is more and more mature, Google Maps, Baidu Maps, Amap is widely used. Human beings get more and more convenience from outdoor positioning technology. Thanks to the mature outdoor positioning technology, people can easily query the services of all kinds of shops nearby. At the same time, it is easy to get the best routes, even bus routes, self-driving routes and walking routes. Since outdoor positioning technology can bring so much convenience to mankind, so. People begin to focus on indoor positioning technology, how to locate the position of objects in the indoor precision has become an urgent problem. This paper starts with a large number of experiments, collects enough positioning data, through function fitting. The training process of neural network and filter prediction proposed that the angle between the object and the signal base station can be trained by measuring the signal strength, and then the coordinates of the object can be obtained by triangulation. Finally, a whole set of location schemes for the filtering prediction of position coordinates is proposed. The specific work of the whole method is as follows: first, the location experiments are carried out in the outdoor ideal environment respectively. Location experiments in ideal indoor environment and in complex indoor environment. In the ideal indoor and outdoor environment, this paper uses the method of function fitting to analyze the location. It is found that the precision of angle and tie is 5.and the accuracy of positioning is 70. When the indoor environment is complex, the method of function fitting is no longer applicable. So this paper uses the neural network method to train the data, and improves the positioning accuracy of 180cm to 65cm. Secondly, in order to further improve the positioning accuracy and positioning accuracy. In this paper, based on the neural network filter prediction method is used to modify the data, including two main ways, one is coordinate linear prediction algorithm, through the linear superposition of BP neural network and basic Kalman filter. The output vector of the neural network is taken as the observation variable in the filter prediction to realize the filter prediction of the output vector. The second is the network weight filtering algorithm, which uses extended Kalman filter to predict the weight matrix of neural network. The two methods can improve the accuracy of location in complex environment. Coordinate linear prediction algorithm shows excellent accuracy of 180cm accuracy to improve to 70. Third, this paper implements a Bluetooth base station based on dual antennas. Android Bluetooth signal acquisition module, location and presentation module and server-side positioning algorithm for indoor complex environment positioning system. The dual-antenna Bluetooth base station is based on the iBeacon Bluetooth base station. The improved signal generator. Android Bluetooth signal acquisition module is the collection of Bluetooth signal data carrier, at the same time, a Android mobile terminal also represents a user. The server is the key part to complete the positioning data. It will analyze the signal strength data from the Android mobile terminal and calculate the location coordinates to be sent to the mobile side. The mobile terminal updates the position in real time according to the position data sent back. The whole system includes the modules of signal data acquisition, localization algorithm realization and location result presentation, and so on, which cooperate with each other. It provides a way for indoor positioning research.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN925

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