Hai Shiji, Na Xitai, Feng Zhihui, Shi Jinshuo, Sun Qingbin
School of Electronic and Information Engineering, Inner Mongolia University, 235 University West Street, Saihan District, Hohhot, 010000, Inner Mongolia, China.
Sci Rep. 2025 Aug 13;15(1):29753. doi: 10.1038/s41598-025-13095-z.
Target tracking for fixed-wing unmanned aerial vehicles (UAVs) in complex urban environments faces challenges including potential target state loss and occlusion by multiple obstacles, typically large vertical structures like high-rise buildings. This necessitates tracking algorithms capable of both target state estimation and prediction. To address this, this paper proposes a Predictive-Estimative Nonlinear Control (PENC) framework. This framework optimizes the UAV-gimbal system's control inputs in real-time to ensure the target remains within the camera's field of view (FOV) despite obstacles, while simultaneously using historical target state measurements to accurately estimate its current state. When target state measurements is lost, PENC dynamically adjusts the measurement noise covariance matrix (R) and the process noise covariance matrix (Q) within the estimator via a unique weight-switching mechanism. This shifts reliance to the target's dynamic model and historical data for state prediction. Simulations conducted in an environment simulating typical urban vertical obstacles demonstrate that the proposed method significantly outperforms both conventional Nonlinear Model Predictive Control (NMPC) and NMPC with Extended Kalman Filtering (NMPC-EKF) in UAV target tracking performance. This improvement is particularly evident during target measurements loss scenarios, effectively ensuring tracking continuity and robustness. Quantitative results show that PENC increases the Target Visibility Percentage (TVP) by up to 14 percentage points, reduces the Mean Recovery Time (MRT) to 0.03 seconds, and lowers the prediction Root Mean Square Error (RMSE) during state loss by [Formula: see text].
在复杂城市环境中，固定翼无人机的目标跟踪面临诸多挑战，包括潜在的目标状态丢失以及被多个障碍物遮挡，这些障碍物通常是诸如高层建筑之类的大型垂直结构。这就需要能够进行目标状态估计和预测的跟踪算法。为解决这一问题，本文提出了一种预测估计非线性控制（PENC）框架。该框架实时优化无人机云台系统的控制输入，以确保尽管存在障碍物，目标仍能保持在相机的视野（FOV）内，同时利用目标状态的历史测量数据来准确估计其当前状态。当目标状态测量值丢失时，PENC通过一种独特的权重切换机制在估计器内动态调整测量噪声协方差矩阵（R）和过程噪声协方差矩阵（Q）。这将依赖转移到目标的动态模型和历史数据上进行状态预测。在模拟典型城市垂直障碍物的环境中进行的仿真表明，所提出的方法在无人机目标跟踪性能方面显著优于传统的非线性模型预测控制（NMPC）和带有扩展卡尔曼滤波的NMPC（NMPC-EKF）。这种改进在目标测量值丢失的情况下尤为明显，有效地确保了跟踪的连续性和鲁棒性。定量结果表明，PENC将目标可见百分比（TVP）提高了多达14个百分点，将平均恢复时间（MRT）降低到0.03秒，并在状态丢失期间将预测均方根误差（RMSE）降低了[公式：见原文]。
