基于RSSI的被动WiFi定位研究
发布时间：2019-01-06 15:32
【摘要】：近年来,随着无线通信技术的迅猛发展,基于位置的服务(LBS)在实际应用中的重要性日趋凸显。由于在建筑密集区域和室内存在较多障碍物阻挡,常用的卫星定位系统的定位性能受到严重影响,因此采用广泛存在的WiFi网络进行室内定位已成为当前研究的热点。论文首先设计了基于接收信号强度(RSSI)的被动WiFi定位系统。该系统主要包括前端AP模块、Socket通信模块、服务器模块以及定位算法模块,采用的是路由器“被动”定位的方法,其优势在于:(1)支持任何未预装APP或定位芯片的Wi Fi设备;(2)定位路由器无需进行硬件改造;(3)系统后台可以直接获得定位数据,无需待定位设备主动上报位置信息。本文通过软硬件的设计,实现了各个模块的预设功能,为接下来的定位算法验证提供实际测试平台。其次,本论文研究一些新方法,从以下三个方面提高定位系统的精度:(1)采用高斯滤波法筛选收集到的RSSI数据,从而过滤掉存在较大误差的点;(2)研究常用的室内传播模型,并对室内环境衰减因子进行测试,进一步获得符合实测环境的传播损耗模型;(3)对比分析现有的定位算法,结合质心算法和极大似然算法,提出一种改进的基于极大似然和加权质心的混合定位算法。最后,将上述研究的新方法应用于本课题的被动WiFi定位平台,搭架了一套完整的定位演示系统;实测结果表示该系统较好的满足了设计性能需求,同时相对于原有定位系统,在定位精度上实现了有效提高。
[Abstract]:In recent years, with the rapid development of wireless communication technology, the importance of location-based service (LBS) in practical applications is becoming increasingly prominent. Because there are many obstacles in the dense building area and indoor, the positioning performance of the commonly used satellite positioning system has been seriously affected. Therefore, indoor positioning using the widely existing WiFi network has become a hot spot of current research. In this paper, a passive WiFi positioning system based on received signal strength (RSSI) is designed. The system mainly includes front-end AP module, Socket communication module, server module and location algorithm module. Its advantages lie in: (1) supporting any Wi Fi device without pre-installed APP or positioning chip; (2) the location router does not need hardware modification; (3) the system can obtain the location data directly in the background, and it is not necessary for the positioning equipment to report the position information actively. Through the design of software and hardware, the presupposition function of each module is realized, which provides a practical test platform for the next localization algorithm verification. Secondly, this paper studies some new methods, from the following three aspects to improve the accuracy of the positioning system: (1) using Gao Si filtering method to filter the collected RSSI data, so as to filter out the existence of large error points; (2) the commonly used indoor propagation model is studied, and the attenuation factor of indoor environment is tested to obtain the propagation loss model which accords with the measured environment. (3) comparing and analyzing the existing localization algorithms, combining centroid algorithm and maximum likelihood algorithm, an improved hybrid localization algorithm based on maximum likelihood and weighted centroid is proposed. Finally, the new method mentioned above is applied to the passive WiFi positioning platform of this subject, and a complete positioning demonstration system is set up. The measured results show that the system meets the design performance requirements well, and the accuracy of the system is improved effectively compared with the original positioning system.
【学位授予单位】：华侨大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN92
[Abstract]:In recent years, with the rapid development of wireless communication technology, the importance of location-based service (LBS) in practical applications is becoming increasingly prominent. Because there are many obstacles in the dense building area and indoor, the positioning performance of the commonly used satellite positioning system has been seriously affected. Therefore, indoor positioning using the widely existing WiFi network has become a hot spot of current research. In this paper, a passive WiFi positioning system based on received signal strength (RSSI) is designed. The system mainly includes front-end AP module, Socket communication module, server module and location algorithm module. Its advantages lie in: (1) supporting any Wi Fi device without pre-installed APP or positioning chip; (2) the location router does not need hardware modification; (3) the system can obtain the location data directly in the background, and it is not necessary for the positioning equipment to report the position information actively. Through the design of software and hardware, the presupposition function of each module is realized, which provides a practical test platform for the next localization algorithm verification. Secondly, this paper studies some new methods, from the following three aspects to improve the accuracy of the positioning system: (1) using Gao Si filtering method to filter the collected RSSI data, so as to filter out the existence of large error points; (2) the commonly used indoor propagation model is studied, and the attenuation factor of indoor environment is tested to obtain the propagation loss model which accords with the measured environment. (3) comparing and analyzing the existing localization algorithms, combining centroid algorithm and maximum likelihood algorithm, an improved hybrid localization algorithm based on maximum likelihood and weighted centroid is proposed. Finally, the new method mentioned above is applied to the passive WiFi positioning platform of this subject, and a complete positioning demonstration system is set up. The measured results show that the system meets the design performance requirements well, and the accuracy of the system is improved effectively compared with the original positioning system.
【学位授予单位】：华侨大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN92
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相关期刊论文 前2条
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