基于低功耗蓝牙的位置指纹定位技术研究

发布时间：2018-04-06 06:03

本文选题：室内无线定位　切入点：低功耗蓝牙　出处：《西安电子科技大学》2015年硕士论文

【摘要】：随着智能移动终端的普及和互联网技术的飞速发展,基于位置的服务越来越受到人们的关注,它在医疗健康、安全监管、推送信息等领域具有巨大的潜力。现如今人们的工作、生活大部分时间在室内进行,而传统的GPS定位系统并不能适应室内复杂多变的环境,所以利用无线技术实现室内定位成为了当今研究的热点。由于传统蓝牙技术的功耗高、穿透性差、信号传输距离短等原因在室内定位领域没有得到广泛的推广。低功耗蓝牙4.0通信标准的发布使得其应用在室内定位中成为可能。它在功耗、传输距离、信号强度等方面都有了明显的改善,因此本文在低功耗蓝牙技术的基础上对室内定位算法进行了研究。本文分析了室内环境中蓝牙信号在室内的分布特征以及影响蓝牙信号强度分布特征的因素,测试了距离、传输路径、设备对接收信号强度的影响程度。由于室内环境复杂多变,采用信号传播模型的定位方法会产生较大的误差,位置指纹定位算法更适用于室内环境中。但是传统位置指纹定位在时间复杂度和精确度上存在不足。所以针对传统位置指纹定位算法的缺陷,本文采取了改进的措施。在建立位置指纹库时,采用k-means聚类和模糊c均值聚类的分析初始的位置指纹库,并划分成多个子类。实时数据不再和初始数据库中的所有数据匹配,而是将它归属到某一个子类中,并和子类中的指纹数据匹配。这样有效地缩减了搜索空间,减少匹配时间的同时还削弱了指纹库对定位结果的影响。在位置估算时,对原有的权重系数进行了改进,使估算类与类间的边界时的精确度得到了提高,类内的点估计不会受到影响,从而提高了整体的定位精度。最后在搭建的测试平台基础上采集实际室内环境中的低功耗蓝牙信号并在matlab上对提出的算法进行了实验验证,结果显示在1m的误差范围内基于模糊c均值的FWKNN算法能够达到80%的概率,证明了改进后的位置指纹定位算法与传统算法相比,定位的精度得到了有效地提升。
[Abstract]:With the popularity of intelligent mobile terminals and the rapid development of Internet technology, location-based services have attracted more and more attention. It has great potential in the fields of health care, safety supervision, push information and so on.Nowadays, people work and live most of the time indoors, but the traditional GPS positioning system can not adapt to the complex and changeable indoor environment, so using wireless technology to achieve indoor positioning has become a hot topic.Because of the high power consumption, poor penetration and short transmission distance of the traditional Bluetooth technology, it has not been widely used in the field of indoor positioning.The release of low power Bluetooth 4.0 communication standard makes its application in indoor positioning possible.It has obvious improvement in power consumption, transmission distance and signal intensity, so this paper studies indoor location algorithm based on low power Bluetooth technology.In this paper, the distribution characteristics of Bluetooth signal in indoor environment and the factors influencing the intensity distribution of Bluetooth signal are analyzed. The influence of distance, transmission path and equipment on the intensity of received signal is tested.Because the indoor environment is complex and changeable, the localization method based on the signal propagation model will produce large errors, and the location fingerprint location algorithm is more suitable for indoor environment.However, the traditional location fingerprint location has some shortcomings in time complexity and accuracy.Therefore, aiming at the defects of the traditional location fingerprint location algorithm, this paper takes some improved measures.When the location fingerprint database is established, the initial location fingerprint database is analyzed by k-means clustering and fuzzy c-means clustering, and is divided into several subclasses.The real-time data is no longer matched with all the data in the initial database, but belongs to a subclass and matches the fingerprint data in the subclass.In this way, the search space is reduced effectively, the matching time is reduced, and the influence of fingerprint database on the location results is weakened.In position estimation, the original weight coefficient is improved to improve the accuracy of estimating the boundary between the class and the class, and the point estimation within the class will not be affected, thus improving the overall positioning accuracy.Finally, the low-power Bluetooth signal in the indoor environment is collected on the basis of the test platform, and the proposed algorithm is tested on matlab.The results show that the probability of FWKNN algorithm based on fuzzy c-means can reach 80% in the error range of 1m. It is proved that the improved location fingerprint location algorithm can improve the accuracy of location effectively compared with the traditional algorithm.
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN925

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