卡尔曼滤波和递推平均滤波在iBeacon室内定位中的应用
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摘要
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。
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.
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.
引文
[1]张梦丹,卢光跃,王宏刚等.基于指纹算法的无线室内定位技术[J].电信科学,2016,32(10):77-86.
[2]刘运杰,金明录,崔承毅.基于RSSI的无线传感器网络修正加权质心定位算法[J].传感技术学报,2010,23(5):717-721.
[3]MAUNG N A M,KAWAI M.Experimental eva luations of RSS threshold-based optimised DV-HOPlocalisation for wireless ad-hoc networks[J].Electronics Letters,2014,50(17):1246-1248.
[4]赵昭,陈小惠.无线传感器网络中基于RSSI的改进定位算法[J].传感技术学报,2009,22(3):391-394.
[5]曹春萍,罗玲莉.基于卡尔曼滤波算法的室内无线定位系统[J].计算机系统应用,2011,20(11):76-79.
[6]赵永翔,周怀北,陈淼等.卡尔曼滤波在室内定位系统实时跟踪中的应用[J].武汉大学学报(理学版),2009,55(6):696-700.
[7]吴成东,程龙,张云洲等.基于循环滤波的无线传感器网络室内定位[J].控制与决策,2011,26(10):1515-1519.
[2]刘运杰,金明录,崔承毅.基于RSSI的无线传感器网络修正加权质心定位算法[J].传感技术学报,2010,23(5):717-721.
[3]MAUNG N A M,KAWAI M.Experimental eva luations of RSS threshold-based optimised DV-HOPlocalisation for wireless ad-hoc networks[J].Electronics Letters,2014,50(17):1246-1248.
[4]赵昭,陈小惠.无线传感器网络中基于RSSI的改进定位算法[J].传感技术学报,2009,22(3):391-394.
[5]曹春萍,罗玲莉.基于卡尔曼滤波算法的室内无线定位系统[J].计算机系统应用,2011,20(11):76-79.
[6]赵永翔,周怀北,陈淼等.卡尔曼滤波在室内定位系统实时跟踪中的应用[J].武汉大学学报(理学版),2009,55(6):696-700.
[7]吴成东,程龙,张云洲等.基于循环滤波的无线传感器网络室内定位[J].控制与决策,2011,26(10):1515-1519.