目前基于无线设备的室内指纹定位技术因为其设备普及且定位准确而受到人们的广泛关注.针对传统室内指纹定位方式中定位阶段特征匹配时没有考虑当前环境相对于基准环境的变化因素这一不足,提出了一个基于CSI(Channel State Information)的自适应修正模型定位算法.该算法通过引入一个衡量当前室内环境变化的指标PEM(Percentage of nonzero Elements)来表示室内人数增加时子载波波动程度的变化;同时又通过设计一个新的修正匹配模型来补偿因多径造成的指纹特征的衰减.实验结果充分证明了该定位方案相比于之前的指纹定位系统FIFS和CSI-MIMO,准确率分别提高了30%和15%.%Currently,WLAN-based indoor fingerprinting positioning system has been attractived owing to the advantages of open access and high accuracy.For the shortage that matching features do not consider the current environment variables for the reference environment in positioning stage of traditional indoor fingerprinting positioning systems,a novel CSI-based adaptive correction model location algorithm was proposed.The algorithm is used to indicate sub-carrier fluctuation level with the increasing of the number of people in indoor environment by introducing a indicator called PEM(Percentage of nonzero Elements),which can measure the changes of current indoor environment.At the same time,the algorithm also designs a new positioning correction matching model to compensate fingerprinting characteristic attenuation caused by multipath.Experiments have fully demonstrated that the new positioning scheme has an accuracy improvement of 30% and 15% respectively over previous fingerprint positioning system FIFS and CSI-MIMO.
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