【摘要】 日常生活中使用GPS(global position system)进行定位,但GPS无法在室内工作,准确地进行室内定位成为研究的热点之一。在早期的研究中,围绕Wi-Fi指纹进行了大量的实验与改进,但Wi-Fi指纹受到环境因素制约,定位误差较大。针对这一问题,提出一种多信息融合的室内定位算法。首先通过Wi-Fi指纹进行粗略的定位,获取Wi-Fi接入设备的MAC地址以及其信号强度RSSI(received signal strength indication),通过kNN(k nearest neighbor)算法进行分类,得到top-n候选集。再通过地磁信号与图片信息进行候选集的过滤。最后利用社交信息,给出人在室内的最终定位结果。在Android平台和服务器上对该系统进行验证,实验结果表明提出的多信息融合的方法比Wi-Fi指纹的定位算法精度明显提高。更多还原

【Abstract】 GPS is used for localization usually, but cannot be applied indoors. It was a popular topic on how to do the indoor localization accurately. In the early research, indoor localization system based on Wi-Fi fingerprints suffer from the accuracy and site survey problems. Therefore, a novel room-level indoor localization system was designed, which solved the localization problem by using multi-information fusion. Firstly, a top-n candidate through the Wi-Fi fingerprint was gotten, kNN classification algorithm was used after MAC address and the RSSI were gotten. The candidate set was filtered by the geomagnetic signal and the image information. Finally, the social information was used to give the final location results of people in the room. The system was validated on smart phone with Android system. The experimental results show that proposed method is more accurate than the Wi-Fi fingerprint localization algorithm.更多还原