基于WiFi的定位引擎软件的设计与实现
发布时间：2018-09-19 10:53
【摘要】：移动设备的爆炸性增长使得人们对移动定位和导航的需求不断增大,作为室外定位的“最后一公里”,室内定位越来越受到人们的关注。但是因为室内环境复杂,而且对定位精度有着比较严格的要求,所以目前还没有比较完善的室内定位技术可以很好的利用。因此,专家学者提出了多种室内定位技术解决方案,每一种技术都有其应用的场景和优缺点。由于基于WiFi的室内定位技术具有覆盖范围广,信息传输速度快,实现成本低等优点成为了人们研究和关注的热点。 本论文研究分析了当前WiFi室内定位的关键技术,并在此基础上设计实现了基于WiFi的定位引擎软件。目前基于WiFi的室内定位技术分为基于传播模型的定位方法和基于指纹匹配的定位方法两种。基于传播模型的定位方法主要通过寻找RSSI值与AP之间的某种传播模型进行定位计算。但是因为无线信号在传播过程中会受到多径传播以及障碍物的阻挡等影响,RSSI值与AP之间并不存在一个确定的传播模型,因此这种室内定位方法并没有获得好的效果。基于指纹匹配的定位方法需要预先在选定位区域中选取参考点采集射频信号进行训练,从而构建信号强度与定位位置之间的映射关系。在定位的时候移动终端实时采集周围AP的射频信号强度,构建未知指纹,然后在离线阶段中建立好的射频指纹库中,查找和该未知指纹最相似的指纹,该指纹对应的位置就是终端的估计位置。目前基于指纹匹配的室内定位算法基本上都依赖于具体的RSSI值,由于RSSI值的时变性以及设备异构性,所以这种定位方法的定位精度仍然不是很理想。 鉴于以上原因,本文介绍了另一种基于指纹匹配的定位算法,该算法不再依赖于具体的RSSI值,而是通过建立AP和AP之间的某种关系进行定位计算,一般来说这种关系对不同的设备来说都是比较固定的,所以这种定位算法很好的解决了设备异构性问题,具有很好的鲁棒性。基于该算法,设计并实现了具有鲁棒性高精度的室内WiFi定位引擎软件。通过大量的实地测试证明,该定位引擎软件的精度较好,达到了3米左右。
[Abstract]:With the explosive growth of mobile devices, the demand for mobile positioning and navigation is increasing. As the last kilometer of outdoor positioning, indoor positioning has attracted more and more attention. However, due to the complex indoor environment and the strict requirements of positioning accuracy, there is no perfect indoor positioning technology can be used. Therefore, experts and scholars put forward a variety of indoor positioning technology solutions, each technology has its application scenarios and advantages and disadvantages. Because of the advantages of indoor positioning technology based on WiFi, such as wide coverage, fast information transmission and low cost, it has become a hot topic of research and attention. In this paper, the key technologies of WiFi indoor positioning are analyzed, and the software of positioning engine based on WiFi is designed and implemented. At present, the indoor localization technology based on WiFi is divided into two kinds: one is based on propagation model and the other is based on fingerprint matching. The localization method based on propagation model is mainly based on finding a certain propagation model between RSSI value and AP. However, due to the influence of multipath propagation and obstacle blocking on wireless signal propagation, there is not a definite propagation model between RSSI and AP, so this indoor localization method has not achieved good results. The location method based on fingerprint matching needs to select a reference point in the selected location area to collect RF signals for training in order to construct the mapping relationship between the signal strength and the location position. At the time of location, the mobile terminal collects the radio frequency signal intensity of the surrounding AP in real time, constructs the unknown fingerprint, and then in the off-line stage establishes the RF fingerprint database, looks for the fingerprint which is the most similar to the unknown fingerprint. The corresponding position of the fingerprint is the estimated position of the terminal. At present, the indoor location algorithms based on fingerprint matching basically depend on the specific RSSI value. Because of the time-varying of RSSI value and the heterogeneity of equipment, the localization accuracy of this method is still not very good. In view of the above reasons, this paper introduces another location algorithm based on fingerprint matching, which no longer depends on the specific RSSI value, but by establishing a certain relationship between AP and AP. Generally speaking, this relationship is relatively fixed for different devices, so this location algorithm solves the problem of device heterogeneity very well and has good robustness. Based on this algorithm, a robust indoor WiFi positioning engine software is designed and implemented. Through a lot of field tests, it is proved that the accuracy of the positioning engine software is good, reaching about 3 meters.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN92;TP311.52
本文编号：2249924
[Abstract]:With the explosive growth of mobile devices, the demand for mobile positioning and navigation is increasing. As the last kilometer of outdoor positioning, indoor positioning has attracted more and more attention. However, due to the complex indoor environment and the strict requirements of positioning accuracy, there is no perfect indoor positioning technology can be used. Therefore, experts and scholars put forward a variety of indoor positioning technology solutions, each technology has its application scenarios and advantages and disadvantages. Because of the advantages of indoor positioning technology based on WiFi, such as wide coverage, fast information transmission and low cost, it has become a hot topic of research and attention. In this paper, the key technologies of WiFi indoor positioning are analyzed, and the software of positioning engine based on WiFi is designed and implemented. At present, the indoor localization technology based on WiFi is divided into two kinds: one is based on propagation model and the other is based on fingerprint matching. The localization method based on propagation model is mainly based on finding a certain propagation model between RSSI value and AP. However, due to the influence of multipath propagation and obstacle blocking on wireless signal propagation, there is not a definite propagation model between RSSI and AP, so this indoor localization method has not achieved good results. The location method based on fingerprint matching needs to select a reference point in the selected location area to collect RF signals for training in order to construct the mapping relationship between the signal strength and the location position. At the time of location, the mobile terminal collects the radio frequency signal intensity of the surrounding AP in real time, constructs the unknown fingerprint, and then in the off-line stage establishes the RF fingerprint database, looks for the fingerprint which is the most similar to the unknown fingerprint. The corresponding position of the fingerprint is the estimated position of the terminal. At present, the indoor location algorithms based on fingerprint matching basically depend on the specific RSSI value. Because of the time-varying of RSSI value and the heterogeneity of equipment, the localization accuracy of this method is still not very good. In view of the above reasons, this paper introduces another location algorithm based on fingerprint matching, which no longer depends on the specific RSSI value, but by establishing a certain relationship between AP and AP. Generally speaking, this relationship is relatively fixed for different devices, so this location algorithm solves the problem of device heterogeneity very well and has good robustness. Based on this algorithm, a robust indoor WiFi positioning engine software is designed and implemented. Through a lot of field tests, it is proved that the accuracy of the positioning engine software is good, reaching about 3 meters.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN92;TP311.52
【参考文献】
相关期刊论文 前2条
1 邓中亮;王文杰;徐连明;;一种基于K-means算法的WLAN室内定位楼层判别方法[J];软件;2012年12期
2 卢恒惠;刘兴川;张超;林孝康;;基于三角形与位置指纹识别算法的WiFi定位比较[J];移动通信;2010年10期
，本文编号：2249924
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