The poor real-time performance and target occlusion occurred easily when the UAV was tracking the target. In this paper, a target tracking method based on the Back Propagation neural network fusion Kalman filter algorithm was developed to solve the position prediction problem of the UAV target tracking in real time. Firstly, the target tracking algorithm was used to acquire the center position coordinates of the target on the onboard computer, and then the coordinate difference matrix was constructed to train the BP neural network in real time. Secondly, when the target was occluded by the obstacles judged by the Bhattacharyya coefficient, the BP neural network fusion Kalman filter algorithm was used to accurately predict the center position coordinates of the occluded target. Then the flight speed of UAV was calculated by the deviation between the coordinates of the target and the image center. Finally, the velocity command was sent to the UAV by the onboard computer. The experimental results shown that the target position predicted by BP neural network fusion Kalman filter algorithm was more accurate and robust in predicting the center position coordinates of the target, and the UAV can track the moving target on the ground stably.

当无人机跟踪目标时，很容易发生实时性能差和目标遮挡的问题。本文提出了一种基于反向传播神经网络融合卡尔曼滤波算法的目标跟踪方法，以解决无人机目标跟踪的实时位置预测问题。首先，使用目标跟踪算法在车载计算机上获取目标的中心位置坐标，然后构造坐标差矩阵以实时训练BP神经网络。其次，当目标被Bhattacharyya系数判断的障碍物遮挡时，使用BP神经网络融合卡尔曼滤波算法准确地预测了被遮挡目标的中心位置坐标。然后，通过目标与图像中心坐标之间的偏差来计算无人机的飞行速度。最后，速度命令由机载计算机发送到无人机。实验结果表明，BP神经网络融合卡尔曼滤波算法预测的目标位置在预测目标的中心位置坐标时更加准确，鲁棒，无人机可以稳定地跟踪地面上的运动目标。