基于GNSS阵列的UKF在流形上实现多路径检测
GNSS Array-Based Multipath Detection Employing UKF on Manifolds
AI总结：
本文提出基于GNSS阵列和UKF在流形上的多路径检测方法，利用RANSAC算法提高计算效率，有效提升定位精度。
摘要：
全球导航卫星系统（GNSS）应用常常受到多种误差源的阻碍，其中多路径干扰是其中最具挑战性的，尤其是在城市环境中。在本工作中，我们基于先前的研究，实现了基于GNSS阵列的多路径检测算法，整合了实时姿态估计以应对动态场景。该方法利用Unscented Kalman Filter（UKF）在流形上融合GNSS和IMU数据，实现连续的姿态跟踪。所提出的方法利用卫星组合的姿态信息来识别并排除受多路径干扰的卫星，提高定位和姿态确定的准确性。为了解决评估大量卫星组合所带来的计算挑战，我们提出使用随机采样一致性（RANSAC）算法，该算法在减少评估组合数量的同时保持了高性能。性能评估使用KITTI数据集中的轨迹和IMU读数进行。GNSS观测基于真实位置和卫星历元进行模拟。结果表明，所提出的方法在检测受多路径干扰的卫星方面是有效的。观察到显著的定位精度改进，特别是在可见卫星大部分受严重多路径干扰的情况下。
英文摘要：
Global Navigation Satellite Systems (GNSS) applications are often hindered by various sources of error, with multipath interference being one of the most challenging, particularly in urban environments. In this work, we build on previous research by implementing a GNSS array-based multipath detection algorithm, incorporating real-time attitude estimation for dynamic scenarios. The method fuses GNSS and IMU data using an Unscented Kalman Filter (UKF) on a manifold, enabling continuous attitude tracking. The proposed approach utilizes attitude information from satellite combinations to identify and exclude multipath-affected satellites, improving the accuracy of both positioning and attitude determination. To address computational challenges associated with evaluating large numbers of satellite combinations, we propose the use of the Random Sample Consensus (RANSAC) algorithm, which reduces the number of combinations assessed while maintaining high detection performance. Performance evaluations are conducted using trajectories and IMU readings from the KITTI dataset. GNSS observations are simulated based on ground truth positions and satellite ephemeris. The results demonstrate the effectiveness of the proposed approach in detecting satellites affected by multipath interference. Significant improvements in positioning accuracy are observed, particularly in scenarios where a large portion of the visible satellites are contaminated by severe multipath.
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