A Novel Optimal Robust Adaptive Scheme for Accurate GNSS RTK/INS Tightly Coupled Integration in Urban Environments

作者：Jiaji Wu, Jinguang Jiang, Chao Zhang, Yuying Li, Peihui Yan, Xiaoliang Meng · 发表于：Remote Sensing · 年份：2023 · DOI：10.3390/rs15153725 · 被引用次数：12 · 研究领域：Inertial Sensor and Navigation、GNSS positioning and interference、Target Tracking and Data Fusion in Sensor Networks

Modern navigation systems are inseparable from an integrated solution consisting of a global navigation satellite system (GNSS) and an inertial navigation system (INS) since they serve as an important cornerstone of national comprehensive positioning, navigation, and timing (PNT) technology and can provide position, velocity, and attitude information at higher accuracy and better reliability. A robust adaptive method utilizes the observation information of both systems to optimize the filtering system, overcoming the shortcomings of the Kalman filter (KF) in complex urban environments. We propose a novel robust adaptive scheme based on a multi-condition decision model suitable for tightly coupled real-time kinematic (RTK)/INS architecture, which can reasonably determine whether the filtering system performs robust estimation (TCRKF) or adaptive filtering (TCAKF), improving the robust estimation method of two factors considering ambiguity variance for RTK-related observations. The performance of the proposed robust adaptive algorithm was evaluated through two sets of real vehicle tests. Compared with the TCAKF and TCRKF algorithms, the new robust adaptive scheme improves the average three-dimensional (3D) position root mean square (RMS) by 31% and 18.88%, respectively. It provides better accuracy and reliability for position, velocity, and attitude simultaneously.