A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental e...A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental environmental changes,such as AP movement. In order to address this problem,a robust fingerprinting indoor localization method is initiated. In the offline phase,three attributes of Received Signal Strength Indication( RSSI) —average,standard deviation and AP's response rate—are computed to prepare for the subsequent computation. In this way,the underlying location-relevant information can be captured comprehensively. Then in the online phase, a three-step voting scheme-based decision mechanism is demonstrated, detecting and eliminating the part of AP where the signals measured are severely distorted by AP 's movement. In the following localization step,in order to achieve accuracy and efficiency simultaneously,a novel fingerprinting localization algorithm is applied. Bhattacharyya distance is utilized to measure the RSSI distribution distance,thus realizing the optimization of MAximum Overlapping algorithm( MAO). Finally,experimental results are displayed,which demonstrate the effectiveness of our proposed methods in eliminating outliers and attaining relatively higher localization accuracy.展开更多
Wireless node localization is one of the key technologies for wireless sensor networks. Outdoor localization can use GPS, AGPS (Assisted Global Positioning System) [6], but in buildings like supermarkets and undergrou...Wireless node localization is one of the key technologies for wireless sensor networks. Outdoor localization can use GPS, AGPS (Assisted Global Positioning System) [6], but in buildings like supermarkets and underground parking, the accuracy of GPS and even AGPS will be greatly reduced. Since Indoor localization requests higher accuracy, using GPS or AGPS for indoor localization is not feasible in the current view. RSSI-based trilateral localization algorithm, due to its low cost, no additional hardware support, and easy-understanding, it becomes the mainstream localization algorithm in wireless sensor networks. With the development of wireless sensor networks and smart devices, the number of WIFI access point in these buildings is increasing, as long as a mobile smart device can detect three or three more known WIFI hotspots’ positions, it would be relatively easy to realize self-localization (Usually WIFI access points locations are fixed). The key problem is that the RSSI value is relatively vulnerable to the influence of the physical environment, causing large calculation error in RSSI-based localization algorithm. The paper proposes an improved RSSI-based algorithm, the experimental results show that compared with original RSSI-based localization algorithms the algorithm improves the localization accuracy and reduces the deviation.展开更多
随着移动互联网的发展,人们对于室内的位置服务需求日益增加。基于Wi-Fi的指纹库室内定位算法具有成本低、定位误差小的优点,但指纹库信号采集需要消耗大量的时间和人力,本文对稀疏参考点下构建高效指纹数据库和高精度室内定位的方法进...随着移动互联网的发展,人们对于室内的位置服务需求日益增加。基于Wi-Fi的指纹库室内定位算法具有成本低、定位误差小的优点,但指纹库信号采集需要消耗大量的时间和人力,本文对稀疏参考点下构建高效指纹数据库和高精度室内定位的方法进行了深入研究。本文改进了卡尔曼滤波有效解决了Wi-Fi的噪声和缺失点,设计了基于信号强度差分方差的无线接入点筛选策略来滤除信息量较低的接入点,提出了一种基于支持向量回归拟合的克里金插值算法(Kriging Interpolation Algorithm Based On Support Vector Regression,SVR-Kriging)进行指纹库的构建,最后通过接入点加权的K加权近邻法(AP weighted and Weighted K-Nearest Neighbor,AWKNN)完成定位。将该方法应用于实际的二维、三维定位场景,实验结果表明二维场景平均定位误差为1.01 m,三维场景平均定位误差为0.92 m。该方法解决了指纹数据库信号采集困难、接入点数据冗余的问题,有效地降低了定位误差。展开更多
基金Sponsored by the National High Technology Research and Development Program of China(Grant No.2014AA123103)
文摘A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental environmental changes,such as AP movement. In order to address this problem,a robust fingerprinting indoor localization method is initiated. In the offline phase,three attributes of Received Signal Strength Indication( RSSI) —average,standard deviation and AP's response rate—are computed to prepare for the subsequent computation. In this way,the underlying location-relevant information can be captured comprehensively. Then in the online phase, a three-step voting scheme-based decision mechanism is demonstrated, detecting and eliminating the part of AP where the signals measured are severely distorted by AP 's movement. In the following localization step,in order to achieve accuracy and efficiency simultaneously,a novel fingerprinting localization algorithm is applied. Bhattacharyya distance is utilized to measure the RSSI distribution distance,thus realizing the optimization of MAximum Overlapping algorithm( MAO). Finally,experimental results are displayed,which demonstrate the effectiveness of our proposed methods in eliminating outliers and attaining relatively higher localization accuracy.
文摘Wireless node localization is one of the key technologies for wireless sensor networks. Outdoor localization can use GPS, AGPS (Assisted Global Positioning System) [6], but in buildings like supermarkets and underground parking, the accuracy of GPS and even AGPS will be greatly reduced. Since Indoor localization requests higher accuracy, using GPS or AGPS for indoor localization is not feasible in the current view. RSSI-based trilateral localization algorithm, due to its low cost, no additional hardware support, and easy-understanding, it becomes the mainstream localization algorithm in wireless sensor networks. With the development of wireless sensor networks and smart devices, the number of WIFI access point in these buildings is increasing, as long as a mobile smart device can detect three or three more known WIFI hotspots’ positions, it would be relatively easy to realize self-localization (Usually WIFI access points locations are fixed). The key problem is that the RSSI value is relatively vulnerable to the influence of the physical environment, causing large calculation error in RSSI-based localization algorithm. The paper proposes an improved RSSI-based algorithm, the experimental results show that compared with original RSSI-based localization algorithms the algorithm improves the localization accuracy and reduces the deviation.
文摘随着移动互联网的发展,人们对于室内的位置服务需求日益增加。基于Wi-Fi的指纹库室内定位算法具有成本低、定位误差小的优点,但指纹库信号采集需要消耗大量的时间和人力,本文对稀疏参考点下构建高效指纹数据库和高精度室内定位的方法进行了深入研究。本文改进了卡尔曼滤波有效解决了Wi-Fi的噪声和缺失点,设计了基于信号强度差分方差的无线接入点筛选策略来滤除信息量较低的接入点,提出了一种基于支持向量回归拟合的克里金插值算法(Kriging Interpolation Algorithm Based On Support Vector Regression,SVR-Kriging)进行指纹库的构建,最后通过接入点加权的K加权近邻法(AP weighted and Weighted K-Nearest Neighbor,AWKNN)完成定位。将该方法应用于实际的二维、三维定位场景,实验结果表明二维场景平均定位误差为1.01 m,三维场景平均定位误差为0.92 m。该方法解决了指纹数据库信号采集困难、接入点数据冗余的问题,有效地降低了定位误差。
