In recent years,WiFi indoor positioning technology has become a hot research topic at home and abroad.However,at present,indoor positioning technology still has many problems in terms of practicability and stability,w...In recent years,WiFi indoor positioning technology has become a hot research topic at home and abroad.However,at present,indoor positioning technology still has many problems in terms of practicability and stability,which seriously affects the accuracy of indoor positioning and increases the complexity of the calculation process.Aiming at the instability of RSS and the more complicated data processing,this paper proposes a low-frequency filtering method based on fast data convergence.Low-frequency filtering uses MATLAB for data fitting to filter out low-frequency data;data convergence combines the mean and multi-data parallel analysis process to achieve a good balance between data volume and system performance.At the same time,this paper combines the position fingerprint and the relative position method in the algorithm,which reduces the error on the algorithm system.The test results show that the strategy can meet the requirements of indoor passive positioning and avoid a large amount of data collection and processing,and the average positioning error is below 0.5 meters.展开更多
文摘In recent years,WiFi indoor positioning technology has become a hot research topic at home and abroad.However,at present,indoor positioning technology still has many problems in terms of practicability and stability,which seriously affects the accuracy of indoor positioning and increases the complexity of the calculation process.Aiming at the instability of RSS and the more complicated data processing,this paper proposes a low-frequency filtering method based on fast data convergence.Low-frequency filtering uses MATLAB for data fitting to filter out low-frequency data;data convergence combines the mean and multi-data parallel analysis process to achieve a good balance between data volume and system performance.At the same time,this paper combines the position fingerprint and the relative position method in the algorithm,which reduces the error on the algorithm system.The test results show that the strategy can meet the requirements of indoor passive positioning and avoid a large amount of data collection and processing,and the average positioning error is below 0.5 meters.
文摘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。
