卡尔曼滤波和递推平均滤波在iBeacon室内定位中的应用

为了减少室内定位过程中出现的定位结果跳变和定位结果不连续问题,提高定位结果的稳定性与可靠性,在iBeacon室内定位系统中引入卡尔曼滤波算法和递推平均滤波算对移动定位结果进行滤波优化处理。首先,使用卡尔曼波算法对移动定位结果进行第一次滤波;然后,使用递推平均滤波算法对第一次滤波之后的定位结果进行二滤波。实验结果表明,在地下停车场环境下,该方法对定位过程中出现的定位结果跳变现象抑制效果明显,同时使定位结果的连续性大大增强,相比于传统的采用单一的滤波算法,该方法使移动定位结果的稳定性和可靠性都得到提高。

Application of Kalman Filter and Recursive Average Filter in iBeacon Indoor Positioning System

In order to improve the stability and reliability of positioning results and reduce the possibility of problems such as the jumping of positioning results and the uncontinuity of positioning results during the indoor positioning process, the Kalman filter algorithm and Recursive average filter algorithm are introduced into an iBeacon indoor positioning system to optimize the mobile positioning results. Firstly, the Kalman filter algorithm is used to filter the positioning results for rhe first time,Secondly, the Recursive average filter is used to filter the positioning results which already filtered once by the Kalman filter algorithm before. Experimental results show that the jumping of positioning results can be reduced obviously by using this method in the underground parking garage environment and can improve the continuity of positioning results. Compared with the traditional methoid which use single filtering algorithm, this method can improve the stability and reliability of positioning results during the mobile positionin process.