| 基于无迹卡尔曼滤波的无人机跟踪算法 |
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| 引用本文： | 罗正华,陈嘉伟,蒋霓,刘一达.基于无迹卡尔曼滤波的无人机跟踪算法[J].成都大学学报(自然科学版),2020(1):55-59. |
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| 作者姓名： | 罗正华 陈嘉伟 蒋霓 刘一达 |
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| 作者单位： | ;1.成都大学信息科学与工程学院 |
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| 基金项目： | 四川省科技厅科技计划(2018JZ0065、2018GZ0509)资助项目。 |
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| 摘 要： | 基于四基站对无人机位置的定位数据,利用无迹卡尔曼滤波算法对定位数据进行最优估计,并预测无人机的运行轨迹,从而实现对无人机的实时跟踪.对经典的线性卡尔曼滤波算法和无迹卡尔曼滤波算法进行仿真对比,结果表明,线性卡尔曼滤波算法虽然能跟踪预测轨迹,但有较大的误差,而使用无迹卡尔曼滤波算法能有效地减小误差,使跟踪预测的轨迹更加精确.
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| 关 键 词： | 四基站定位 无迹卡尔曼滤波算法 跟踪预测 |
Unmanned Aerial Vehicle Tracking Algorithm Based on Unscented Kalman Filter |
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| LUO Zhenghua,CHEN Jiawei,JIANG Ni,LIU Yida.Unmanned Aerial Vehicle Tracking Algorithm Based on Unscented Kalman Filter[J].Journal of Chengdu University (Natural Science),2020(1):55-59. |
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| Authors: | LUO Zhenghua CHEN Jiawei JIANG Ni LIU Yida |
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| Institution: | (School of Information Science and Engineering,Chengdu university,Chengdu 610106,China) |
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| Abstract: | Based on the positioning position data of unmanned aerial vehicle(UAV)based on four-base stations,Kalman filter algorithm is used to estimate the positioning data optimally,and the trajectory of UAV is predicted,so as to realize the realtime tracking of UAV.The simulation results of the classical linear Kalman filter algorithm and the nonlinear unscented Kalman filter algorithm show that although the linear Kalman filter algorithm can track the prediction trajectory,but it has a large error,and the unscented Kalman filter algorithm can effectively reduce the error and make the trajectory of tracking and prediction more accurate. |
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| Keywords: | four-base station positioning unscented Kalman filtering algorithm track prediction |
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