MAV集群中基于超宽带测距的协同相对定位
Cooperative Relative Localization in MAV Swarms with Ultra-wideband Ranging
AI总结：
针对GPS拒止环境下MAV集群的相对定位难题，提出基于UWB测距的协同相对定位方案，利用邻居间距离作为额外信息扩展可观测子空间，并采用LV核EKF处理重尾噪声，显著提升估计精度与鲁棒性。
摘要：
相对定位（RL）对于微型飞行器（MAV）集群的成功运行至关重要。在无基础设施且GPS拒止的环境中，仅利用距离信息实现精确的三维相对定位是一个尚未得到满意解决的挑战性问题。在本工作中，基于采用超宽带（UWB）测距技术的测距式点对点相对定位，我们开发了一种新颖的基于UWB的协同相对定位（CRL）解决方案，该方案整合每个主机-邻居对的相对运动动力学以构建统一动态模型，并将邻居之间的距离作为\textit{额外信息}。使用微分几何的可观测性分析表明，与仅使用主机代理与其邻居之间直接距离的其他替代方案相比，所提出的CRL方案能够扩展可观测子空间。此外，我们将核诱导扩展卡尔曼滤波器（EKF）应用于CRL状态估计问题，采用新设计的对数-Versoria（LV）核以处理重尾UWB噪声。还推导了估计算法中涉及的定点迭代收敛的充分条件。对比蒙特卡洛模拟表明，所提出的CRL方案结合LV核EKF，由于其对测量异常值和错误测量协方差矩阵初始化的鲁棒性，显著提高了估计精度。此外，使用LV核时，即使仅执行一次定点迭代以减少计算复杂度，估计结果仍然令人满意。
英文摘要：
Relative localization (RL) is essential for the successful operation of micro air vehicle (MAV) swarms. Achieving accurate 3-D RL in infrastructure-free and GPS-denied environments with only distance information is a challenging problem that has not been satisfactorily solved. In this work, based on the range-based peer-to-peer RL using the ultra-wideband (UWB) ranging technique, we develop a novel UWB-based cooperative relative localization (CRL) solution that integrates the relative motion dynamics of each host-neighbor pair to build a unified dynamic model and takes the distances between the neighbors as \textit{bonus information}. Observability analysis using differential geometry shows that the proposed CRL scheme can expand the observable subspace compared to other alternatives using only direct distances between the host agent and its neighbors. In addition, we apply the kernel-induced extended Kalman filter (EKF) to the CRL state estimation problem with the novel-designed Logarithmic-Versoria (LV) kernel to tackle heavy-tailed UWB noise. Sufficient conditions for the convergence of the fixed-point iteration involved in the estimation algorithm are also derived. Comparative Monte Carlo simulations demonstrate that the proposed CRL scheme combined with the LV-kernel EKF significantly improves the estimation accuracy owing to its robustness against both measurement outliers and incorrect measurement covariance matrix initialization. Moreover, with the LV kernel, the estimation is still satisfactory when performing the fixed-point iteration only once for reduced computational complexity.
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