城市环境下GNSS退化时四旋翼无人机导航的信念自适应在线自主性

中文摘要 AI 辅助

可靠的在线自主性对于四旋翼无人机在城市空域中的运行至关重要，因为全球导航卫星系统（GNSS）测量会受到多径、遮挡和延迟问题的影响，引入非平稳、时间相关的误差，从而降低传统GNSS-IMU融合的性能。本文提出了一种信念自适应在线自主性框架，该框架通过显式GNSS信任建模、二阶在线信念自适应以及延迟感知的乱序测量处理来增强扩展卡尔曼滤波器（EKF）。GNSS信任被表示为潜在信念状态，用于调节测量权重和多径偏差不确定性，并利用EKF一致性信号在线更新。与反应式协方差调整不同，所提出的方法能够在没有先验环境知识或离线训练的情况下实现主动且稳定的传感器信任自适应。在具有相关多径、随机延迟和障碍约束的模拟城市空中交通场景中的评估表明，与朴素、自适应和一阶基线相比，该方法改善了信念收敛性，使轨迹更平滑，并减少了估计和跟踪误差。该框架保留了经典GNSS-IMU融合结构，可直接集成到现有飞行控制流程中，支持在GNSS退化环境中的稳健在线自主性。

英文摘要

Reliable online autonomy is critical for quadrotor operation in urban airspaces, where global navigation satellite systems (GNSS) measurements suffer from multipath, blockage, and latency issues, introducing non-stationary, temporally correlated errors that degrade conventional GNSS-IMU fusion. This paper presents a belief-adaptive online autonomy framework that augments an extended Kalman filter (EKF) with explicit GNSS trust modelling, second-order online belief adaptation, and latency-aware out-of-sequence measurement handling. GNSS trust is represented as a latent belief state that modulates measurement weighting and multipath bias uncertainty, and is updated online using EKF consistency signals. Unlike reactive covariance tuning, the proposed approach enables proactive and stable sensor trust adaptation without prior environmental knowledge or offline training. Evaluation in simulated urban air mobility scenarios with correlated multipath, stochastic latency, and obstacle constraints demonstrates improved belief convergence, smoother trajectories, and reduced estimation and tracking errors compared to naive, adaptive, and first-order baselines. The framework preserves classical GNSS-IMU fusion structure and can be integrated directly into existing flight control pipelines, supporting robust online autonomy in GNSS degraded environments.

发表机构

• Cranfield University（克兰菲尔德大学）

机构由 AI 辅助整理，请以论文原文为准。