基于卡尔曼滤波的室内定位方法设计
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摘要
针对室内环境难以依靠GPS进行精确定位的问题,设计了一种基于卡尔曼滤波的室内物体定位方法。该模型利用RSSI中的Shadowing传播模型来估算待定位节点与发送节点之间的距离,建立关于待定位节点和发送节点距离的目标方程,通过最小二乘算法来估计方程中的系数。为了进一步提高定位的准确性,采用卡尔曼滤波算法优化位置的估计,通过不断迭代获得最终的位置估计值。为了验证所提方法的可行性,在节点静态和节点移动两种情况下,对所提的方法进行验证,结果表明,该方法具有较小的平均定位误差和平均绝对定位误差,与其它方法相比,具有较高的定位精度。
Aiming at the accurate indoor location is difficult to be achieved by using GPS, a localization method based on Kalman filtering algorithm is proposed. The model uses the shadowing broadcasting model to estimate the distance between the node needing to be localized and the sending node, the goal function between the sending node and the receiving node is established. The least square difference algorithm is used to calculate the coefficient of the goal function. To improve the accuracy of the localization, the Kalman filtering algorithm is used to optimize the position of the node. The optimal estimation can be obtained by iterations. To verify the effect of the proposed method, it is implemented under two conditions, static node and moving node. The result shows that the proposed method has the smaller localization error and average absolute error. Compared with the other methods, it has the higher localization accuracy.
Aiming at the accurate indoor location is difficult to be achieved by using GPS, a localization method based on Kalman filtering algorithm is proposed. The model uses the shadowing broadcasting model to estimate the distance between the node needing to be localized and the sending node, the goal function between the sending node and the receiving node is established. The least square difference algorithm is used to calculate the coefficient of the goal function. To improve the accuracy of the localization, the Kalman filtering algorithm is used to optimize the position of the node. The optimal estimation can be obtained by iterations. To verify the effect of the proposed method, it is implemented under two conditions, static node and moving node. The result shows that the proposed method has the smaller localization error and average absolute error. Compared with the other methods, it has the higher localization accuracy.
引文
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[4]李玲霞,郭可可,田增山,等.基于相关性测序的TD-LTE分布式系统室内定位算法[J].电子与信息学报,2018(5):1059-1065.
[5]杨明极,刘恺怿,邵丹.用于WLAN室内定位的PCA聚类算法[J].电信科学,2016(7):21-26.
[6]周瑞,袁兴中,黄一鸣.基于卡尔曼滤波的WiFi-PDR融合室内定位[J].电子科技大学学报,2016(3):399-404.
[7]刘晓晨,张静.基于改进BP神经网络的室内无线定位方法[J].计算机应用与软件,2016(6):114-117.
[2]Lee Y S,Park J W,Barolli L.A localization algorithm based on AOA for Ad-hoc sensor networks[J].Mobile Information System,2012(1):61-72.
[3]石欣,印爱民,陈曦.基于RSSI的多维标度室内定位算法[J].仪器仪表学报,2014(2):261-268.
[4]李玲霞,郭可可,田增山,等.基于相关性测序的TD-LTE分布式系统室内定位算法[J].电子与信息学报,2018(5):1059-1065.
[5]杨明极,刘恺怿,邵丹.用于WLAN室内定位的PCA聚类算法[J].电信科学,2016(7):21-26.
[6]周瑞,袁兴中,黄一鸣.基于卡尔曼滤波的WiFi-PDR融合室内定位[J].电子科技大学学报,2016(3):399-404.
[7]刘晓晨,张静.基于改进BP神经网络的室内无线定位方法[J].计算机应用与软件,2016(6):114-117.