Wang Qianshuai, Li Zeyuan, Peng Jicheng, Lu Kelin
School of Automation, Southeast University, Nanjing 210096, China.
Biomimetics (Basel). 2025 May 20;10(5):336. doi: 10.3390/biomimetics10050336.
This paper addresses the problem of observability analysis and enhancement for UAV target localization and sensor bias estimation with bearing-only measurement. Inspired by the compound eye vision, a bio-inspired observability analysis method is proposed for stochastic systems. Furthermore, a performance metric that can be utilized in UAV trajectory optimization for observability enhancement of the target localization system is formulated based on maximum mean discrepancy. The performance metric and the distance of the UAV relative to the target are utilized as objective functions for trajectory optimization. To determine the decision variables (the UAV's velocity and turn rate) for UAV maneuver decision making, a multi-objective optimization framework is constructed, and is subsequently solved via the nonlinear constrained multi-objective whale optimization algorithm. Finally, the analytical results are validated through numerical simulations and comparative analyses. The proposed method demonstrates superior convergence in both target localization and sensor bias estimation. The nonlinear constrained multi-objective whale optimization algorithm achieves minimal values for both generational distance and inverted generational distance, demonstrating superior convergence and diversity characteristics.
本文研究了基于仅方位测量的无人机目标定位和传感器偏差估计的可观测性分析与增强问题。受复眼视觉启发，针对随机系统提出了一种仿生可观测性分析方法。此外，基于最大均值差异制定了一种性能指标，可用于无人机轨迹优化以增强目标定位系统的可观测性。该性能指标和无人机相对于目标的距离被用作轨迹优化的目标函数。为了确定无人机机动决策的决策变量（无人机的速度和转弯率），构建了一个多目标优化框架，并随后通过非线性约束多目标鲸鱼优化算法进行求解。最后，通过数值模拟和对比分析对分析结果进行了验证。所提方法在目标定位和传感器偏差估计方面均表现出卓越的收敛性。非线性约束多目标鲸鱼优化算法在世代距离和反向世代距离方面均取得了最小值，展现出卓越的收敛性和多样性特征。
