PENC:一个预测估计的非线性控制框架,用于在复杂的城市环境中对固定翼无人机的强有力的目标跟踪
Shiji Hai1, Xitai Na2, Zhihui Feng1
1School of Electronic and Information Engineering, Inner Mongolia University, 235 University West Street, Saihan District, Hohhot, 010000, Inner Mongolia, China.
Scientific reports
|August 13, 2025
概括
一个新的预测估计非线性控制 (PENC) 框架增强了固定翼无人机 (UAV) 在城市地区的目标跟踪. PENC确保连续跟踪和准确的状态预测,即使失去了目标测量.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 计算机视觉和目标跟踪
- 航空航天工程 航空航天工程
背景情况:
- 固定翼无人机 (UAV) 在复杂的城市环境中跟踪目标时面临重大挑战,主要是由于垂直结构的遮蔽.
- 现有的跟踪算法经常与目标状态损失作斗争,需要强大的估计和预测能力来保持运营效率.
研究的目的:
- 引入一种新的预测-估计非线性控制 (PENC) 框架,旨在提高无人机在受阻的城市环境中的目标跟踪性能.
- 提高目标追踪的稳定性和连续性,特别是在目标测量丢失的时期.
主要方法:
- PENC框架实时优化UAV-gimbal控制输入,以保持摄像机视野 (FOV) 中的目标可见性.
- 它采用独特的权重切换机制,在测量丢失时动态调整估计器中的噪声共变矩阵 (R和Q),根据目标的动态模型和历史数据优先预测.
- 模拟是在一个虚拟的城市环境中进行的,其中具有代表性的垂直障碍.
主要成果:
- 在无人机目标追踪方面,PENC显著优于传统的非线性模型预测控制 (NMPC) 和使用扩展卡尔曼过 (NMPC-EKF) 的NMPC.
- 该框架在目标测量损失期间表现出卓越的性能,确保了跟踪连续性和稳定性.
- 定量改进包括目标可见度百分比 (TVP) 的增加高达14个百分点,平均恢复时间 (MRT) 的减少到0.03秒,以及在状态丢失时预测根平均平方误差 (RMSE) 的减少.
结论:
- 拟议的PENC框架为复杂的城市环境的无人机目标追踪提供了实质性的进展.
- 它的适应性在处理测量损失方面提供了强大的和连续的跟踪能力,这对任务成功至关重要.
- 对于现有方法来说,PENC是一个显著的改进,特别是在涉及遮和间歇性数据的具有挑战性的场景中.
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