3D target tracking by UAVs subject to measurement uncertainties

K. Subbarao, M. Ahmed
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引用次数: 5

Abstract

This paper proposes an Extended Kalman Filter (EKF) based approach to track a moving target UAV in 3D. The state of the target UAV is estimated from the range, azimuth, and elevation angle measurements that are assumed to be available from a ground based sensor or from on-board seeker antenna. The chaser vehicle states are also estimated using onboard sensors, namely GPS + IMU. These estimated states (chaser and the target) are utilized in a nonlinear guidance law which provides signals for the velocity, flight path, and course/heading angle to the chaser so as to track the target vehicle. It is to be mentioned that the guidance controller onboard the chaser was designed based on a backstepping like technique. All process and measurement noise are modelled as zero-mean Gaussian processes with known covariances. The performance of the combined estimation and guidance algorithm is shown via extensive simulation.
受测量不确定性影响的无人机三维目标跟踪
提出了一种基于扩展卡尔曼滤波(EKF)的无人机三维运动目标跟踪方法。目标UAV的状态从距离、方位角和仰角测量估计,假定从地面传感器或机载导引头天线可用。跟踪车辆状态也使用车载传感器进行估计,即GPS + IMU。这些估计状态(追逐者和目标)被用于非线性制导律,该律为追逐者提供速度、飞行路径和航向/航向角信号,以跟踪目标飞行器。需要指出的是,追踪器上的制导控制器是基于一种类似后退的技术设计的。所有过程和测量噪声都建模为已知协方差的零均值高斯过程。通过大量的仿真验证了该组合估计和制导算法的性能。
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