基于空间位置约束的稀疏指纹室内定位方法

针对基于位置服务的实际应用需求,分析了现有室内定位技术的局限性,提出一种基于空间位置约束的稀疏指纹定位方法,在数据层有效融合惯导和无线局域网(WLAN)定位信息,充分发挥二者优势协同完成定位任务。首先利用WLAN提供的接收信号强度(RSS)信息构建空间位置指纹数据库,并基于RSS构建稀疏指纹表征与定位模型;鉴于RSS数据易受环境干扰呈现多变性,利用惯导技术对位移状态进行初步估计,并以此作为约束条件构建基于空间位置约束的稀疏指纹定位模型。仿真实验结果表明,所提方法较惯导和稀疏指纹方法在定位精度方面分别提升58%和33%。

Sparse fingerprint indoor localization based on spatial position constraint

For the practical application requirements of location-based services, a sparse fingerprint localization method based on spatialposition constraint is proposed, after fully analyzing the limitations of the existing indoor location technologies. The positioninginformation from inertial navigation system (INS) and wireless local area network (WLAN) are deeply integrated on the data level, tocoordinate the positioning task. Based on the received signal strength (RSS) data provided by WLAN, the spatial-location-fingerprintdatabase is constructed, together with the sparse fingerprint representation and location model. In view of the RSS variability due toenvironmental interferences, the displacement state can be preliminarily estimated by INS, which will be as a constraint condition toconstruct the sparse fingerprint location model based on spatial position constraint. The simulation experimental results show that thepositioning accuracy of this method is improved by 58% and 33% respectively, compared with the INS and sparse fingerprint methods. Itis demonstrated that the proposed model can appropriately compensate the accumulative error of INS, and the motion prediction by INSalso can restrict the jumping and distortion effects of RSS signals to a certain extent.