针对传统指纹定位方法指纹库庞大臃肿、信息冗余,数据处理机制单调的问题,提出了一种融合压缩感知的指纹信息密度凝聚定位算法(Fingerprint Information Density Aggregation Positioning Algorithm based on Compressed Sensing,FIDA)...针对传统指纹定位方法指纹库庞大臃肿、信息冗余,数据处理机制单调的问题,提出了一种融合压缩感知的指纹信息密度凝聚定位算法(Fingerprint Information Density Aggregation Positioning Algorithm based on Compressed Sensing,FIDA),实现了压缩采样、信号恢复到指纹建库、在线定位的双领域交叉映射,两者互补增益有效提升了系统定位能力。由空间特征修正聚类算法完成区域模糊划分,自适应场景特征并包容区域边缘失配RP;从有效性、区分度和可测性多尺度综合评价并筛选区域最优AP子集,以凝聚信息密度。定位匹配选择稀疏贝叶斯算法削弱指纹相关性影响,引入信息序列提升近邻RP权重。实验结果表明,所提方案精简指纹信息效果良好,能够有效凝练指纹库价值信息。定位精度显著优于本领域其他算法,相比传统定位算法具有一定优势,具备较高的潜力和应用价值。展开更多
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
文摘针对传统指纹定位方法指纹库庞大臃肿、信息冗余,数据处理机制单调的问题,提出了一种融合压缩感知的指纹信息密度凝聚定位算法(Fingerprint Information Density Aggregation Positioning Algorithm based on Compressed Sensing,FIDA),实现了压缩采样、信号恢复到指纹建库、在线定位的双领域交叉映射,两者互补增益有效提升了系统定位能力。由空间特征修正聚类算法完成区域模糊划分,自适应场景特征并包容区域边缘失配RP;从有效性、区分度和可测性多尺度综合评价并筛选区域最优AP子集,以凝聚信息密度。定位匹配选择稀疏贝叶斯算法削弱指纹相关性影响,引入信息序列提升近邻RP权重。实验结果表明,所提方案精简指纹信息效果良好,能够有效凝练指纹库价值信息。定位精度显著优于本领域其他算法,相比传统定位算法具有一定优势,具备较高的潜力和应用价值。
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.
