基于单锚点的无人机定位自适应滑动窗口估计器
An Adaptive Sliding Window Estimator for Positioning of Unmanned Aerial Vehicle Using a Single Anchor
AI总结：
提出一种配备可靠性评估器的自适应滑动窗口估计器，通过同时估计状态、噪声协方差和气动阻力，在标准和恶劣环境中实现0.15m均方根误差，优于现有方法。
摘要：
利用单距离锚点结合机载光学惯性里程计的定位技术，为无人机定位提供了一种轻量级解决方案，能够提供多维测量。然而，这种轻量级传感器的性能会随动态环境而变化，且动态模型的保真度也受到环境气流的严重影响。为应对这一挑战，我们提出了一种配备估计可靠性评估器的自适应滑动窗口估计器，可同时估计状态、噪声协方差矩阵和气动阻力。首先基于后验状态和协方差评估气动阻力效应；然后设计增广卡尔曼滤波器预处理多维测量并继承历史信息；随后采用逆Wishart平滑器估计后验状态和协方差矩阵。为抑制潜在发散，设计了可靠性评估器推断估计误差，并基于误差传播确定各传感器保真度。在标准和恶劣环境中进行了大量实验，证明该方法具有适应性和鲁棒性，均方根误差达0.15m，优于现有方法。另进行了真实世界闭环控制实验验证其实际应用能力。
英文摘要：
Localization using a single range anchor combined with onboard optical-inertial odometry offers a lightweight solution that provides multidimensional measurements for the positioning of unmanned aerial vehicles. Unfortunately, the performance of such lightweight sensors varies with the dynamic environment, and the fidelity of the dynamic model is also severely affected by environmental aerial flow. To address this challenge, we propose an adaptive sliding window estimator equipped with an estimation reliability evaluator, where the states, noise covariance matrices and aerial drag are estimated simultaneously. The aerial drag effects are first evaluated based on posterior states and covariance. Then, an augmented Kalman filter is designed to pre-process multidimensional measurements and inherit historical information. Subsequently, an inverse-Wishart smoother is employed to estimate posterior states and covariance matrices. To further suppress potential divergence, a reliability evaluator is devised to infer estimation errors. We further determine the fidelity of each sensor based on the error propagation. Extensive experiments are conducted in both standard and harsh environments, demonstrating the adaptability and robustness of the proposed method. The root mean square error reaches 0.15 m, outperforming the state-of-the-art approach. Real-world close-loop control experiments are additionally performed to verify the estimator's competence in practical application.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
