A tracking controller for unmanned aerial vehicles (UAVs) is developed to track moving targets in the presence of occlusion. The controller can track moving targets based on a bounding box of the target detected by a deep neural network using the you-only-look-once (YOLO) method. The features generated from the YOLO approach relaxes the assumption of continuous availability of the feature points for applications, which facilitates estimation using an unscented Kalman filter (UKF) and the design of image-based tracking controller in this work. The challenge is that when occlusion is present, the bounding box of the moving target becomes unobtainable and makes the estimation diverge. To solve this, a motion model derived by quadratic programming is employed as a process model in the UKF, wherein the estimated velocity is implemented as a feedforward term in the developed tracking controller in order to enhance the tracking performance. Since no motion constraint is assumed for the target, the developed controller can be applied to track various moving targets. Simulations are used to demonstrate the performance of the developed estimator and controller in the presence of occlusion. Experiments are also conducted to verify the efficacy of the developed estimator and controller.

中文翻译：

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无人机对动态和临时遮挡目标的运动预测和鲁棒跟踪

开发了一种用于无人驾驶飞行器 (UAV) 的跟踪控制器，用于在存在遮挡的情况下跟踪移动目标。控制器可以基于深度神经网络使用你只看一次（YOLO）方法检测到的目标的边界框来跟踪移动目标。YOLO 方法生成的特征放宽了对应用程序特征点连续可用性的假设，这有助于在这项工作中使用无迹卡尔曼滤波器 (UKF) 和基于图像的跟踪控制器的设计进行估计。挑战在于，当存在遮挡时，移动目标的边界框变得不可获得，并使估计发散。为了解决这个问题，在 UKF 中采用由二次规划导出的运动模型作为过程模型，其中估计速度在开发的跟踪控制器中作为前馈项实现，以提高跟踪性能。由于没有假设目标的运动约束，开发的控制器可以应用于跟踪各种运动目标。模拟用于证明开发的估计器和控制器在存在遮挡的情况下的性能。还进行了实验以验证开发的估计器和控制器的功效。