论文标题
GNSS/MEMS-INS集成用于使用EKF在Lie组上的无人机导航
GNSS/MEMS-INS Integration for Drone Navigation using EKF on Lie Groups
论文作者
论文摘要
本文基于Kalman过滤的理论过滤，描述了一个扩展的Kalman过滤器，并为GNSS/INS量身定制的用于后处理应用程序的GNSS/INS的松散整合。该方法在矩阵谎言组上采用动态模型，该模型汇总了位置，速度，态度和IMU偏见为谎言组的单个元素。该开发是由无人机传播的差异干涉量SAR（Dinsar）应用程序激励的，该应用需要使用低成本MEMS传感器进行短期飞行任务的高精度导航信息。过滤器和Rauch-tung-Striebel（RTS）均已实现和验证。该论文还提出了一种新型算法，以初始化标题值，以替代基于陀螺仪或磁力计的对准。 Mahalanobis距离和$χ^2 $ -Test在过滤器更新步骤中使用，以解决GNSS测量值的离群拒绝的实际问题。本文使用合成数据来比较基于乘法季节和欧拉角的经典导航方案。最后，实际数据实验表明，基于Lie组的Kalman过滤器要比最先进的商业软件执行更高的Dinsar处理。
Building upon the theory of Kalman Filtering on Lie Groups, this paper describes an Extended Kalman Filter and Smoother for Loosely Coupled Integration of GNSS/INS tailored for post-processing applications. The approach employs a dynamic model on a matrix Lie Group that aggregates position, velocity, attitude, and the IMU biases as a single element of a Lie group. The development was motivated by a drone-borne Differential Interferometric SAR (DinSAR) application, which requires high-precision navigation information for short-flight missions using low-cost MEMS sensors. The filter and the Rauch-Tung-Striebel (RTS) smoother are both implemented and validated. The paper also presents a novel algorithm to initialize the heading value as an alternative to gyro-compassing or magnetometer-based alignments. The Mahalanobis Distance and the $χ^2$-test are employed during the filter update step to address the practical issue of outlier rejection for the GNSS measurements. The paper uses synthetic data to compare classic navigation schemes based on multiplicative quaternions and Euler angles. Finally, real data experiments demonstrate that the Kalman Filter based on Lie Groups performs better DinSAR processing than state-of-the-art commercial software.
