基于TS-KNN的室内定位算法

室内定位是智慧城市的硬性需求,大量智慧城市相关应用都离不开位置服务。主要室内定位技术包括:蓝牙、RFID、UWB、地磁等,但由于成本、部署便捷性等问题,限制了其应用发展。笔者提出了一种基于指纹时序特征的KNN(k-nearest neighbor)定位算法(TS-KNN,timing sequence based KNN),该算法使用当前时刻的指纹进行基准坐标选择,并利用前几个时刻的定位结果对每个基准坐标进行权值修正。在重庆市某广场进行实验测试结果表明,提出的TS-KNN方法与KNN和WKNN等其他算法相比较,具有更高准确率,可有效提高室内定位精度,降低平均定位误差。

Indoor positioning algorithm based on TS-KNN

Indoor positioning is the hard demand of smart cities and many smart city-related applications are inseparable from location services. At present, the main indoor positioning technology includes Bluetooth, RFID, UWB and geomagnetic, etc., but due to such issues as cost, deployment convenience and so on, its applications development are limited. This paper proposes a KNN(K-Neatest Neighbor) localization algorithm based on fingerprint timing features (TS-KNN), which uses the fingerprint of the current moment to select the reference coordinates and the positioning results of the first few moments are used to perform weight correction for each reference coordinate. The experimental test results in a square in Chongqing show that the proposed TS-KNN method is superior to the KNN and the WKNN algorithms, since it can effectively improve the accuracy of indoor positioning and reduce the average positioning error.