基于无迹卡尔曼滤波的无人机毫米波波束跟踪算法
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1.南京航空航天大学航天学院,南京 210016;2.南京航空航天大学电子信息工程学院,南京 211106

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Unscented Kalman Filter for Beam Tracking of UAV Millimeter Wave
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1.College of Astronautics, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China;2.College of Electronic and Information Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, China

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    摘要:

    针对无人机收发端相对运动导致毫米波窄波束无法实时匹配这一问题,提出一种基于无迹卡尔曼滤波的三维波束跟踪方法。该方法首先将波束的俯仰角和方位角作为系统状态向量,对其进行无迹变换,获得采样点集。而后,根据采样点集计算出状态预测值和测量预测值,并以此为基础,根据计算出的卡尔曼增益更新状态向量,获得状态向量的最优估计值。仿真结果表明,此方法满足了无人机实时波束跟踪需求,有效地提高了三维环境下毫米波窄波束的跟踪精度。

    Abstract:

    Aiming at the problem that the narrow beam cannot be matched in real time due to the relative motion of the transmitter and receiver in the millimeter-wave (mmW) communication system of unmanned aerial vehicles (UAVs), a three-dimensional (3D) beam tracking method based on unscented Kalman filter (UKF) is proposed. Firstly, the elevation and the azimuth angle of the beam at the receiver and the transmitter are regarded as the state vector of the system, and the sampling point set is obtained by the unscented transform (UT). Then, the state prediction value and the measurement prediction value are calculated according to the sampling point set. On this basis, the state vector is updated according to the calculated Kalman gain to obtain the optimal estimate of the state vector. Simulations show that the proposed method can improve the beam tracking accuracy of UAV in 3D dynamic environment.

    图1 模拟波束成形系统Fig.1 Analog beamforming system
    图3 三维UKF跟踪算法流程图Fig.3 Flow chart of 3D UKF tracking algorithm
    图4 不同SNR和跟踪时隙下的EOA的均方误差Fig.4 MSE of EOA with different SNRs and tracking slots
    图5 不同阵列尺寸下的EOA的均方误差(σ2=(0.25°)2)Fig.5 MSE of EOA with different array sizes (σ2=(0.25°)2)
    图6 不同阵列尺寸下的EOA的均方误差(σ2=(0.5°)2)Fig.6 MSE of EOA with different array sizes (σ2=(0.5°)2)
    图7 三维UKF与EKF跟踪方法对比Fig.7 Comparison of 3D UKF and EKF tracking methods
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李鹏辉,仲伟志,张璐璐,杨卓明,朱秋明,陈小敏.基于无迹卡尔曼滤波的无人机毫米波波束跟踪算法[J].数据采集与处理,2021,36(6):1117-1124

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  • 收稿日期:2020-07-20
  • 最后修改日期:2020-10-09
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  • 在线发布日期: 2021-11-25