摘要
为了解决室内目标跟踪系统中由于定位误差导致目标运动轨迹波动较大的问题,提出一种基于最大似然估计与卡尔曼滤波的融合目标跟踪算法。首先利用最大似然估计算法预测目标的运动轨迹,然后再利用卡尔曼滤波算法对预测结果进行滤波处理,进一步降低定位结果的误差。仿真结果表明,所提算法的定位误差均值为0.64m,比通用的最邻近算法性能提升了46.2%,有效提高了系统的精确度及稳定性。
In order to solve the problem of the large jitter in target trajectory due to positioning error in the indoor target tracking system,this paper proposes a fusion target tracking algorithm based on maximum likelihood estimation and Kalman filter.Specifically,the trajectory of target is first predicted by the maximum likelihood estimation algorithm,and then the Kalman filter algorithm is adopted to filter the prediction results to further reduce the positioning error.Simulation results show that the average positioning error of the proposed algorithm is 0.64m,which is 46.2%higher than the performance of the general nearest neighbor algorithm,and hence the accuracy and the stability of the system are efficiently enhanced.
作者
张昕
容荣
ZHANG Xin;RONG Rong(Guangzhou GCI Science&Technology Co.,Ltd.,Guangzhou 510310,China)
出处
《移动通信》
2021年第3期86-90,共5页
Mobile Communications
基金
2019年广州市产业技术重大攻关计划(201902010053)。
关键词
室内定位
最大似然估计
卡尔曼滤波
indoor positioning
maximum likelihood estimation
Kalman filter
