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

Abstract

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.

Mobile Laser Scanning Data Collected under a Forest Canopy with GNSS/INS-Positioned Systems: Possibilities of Processability Improvements

“…Advanced methods of GNSS/INS integration might be constructive and beneficial. Most prospective integration approaches by [65] are considered loosely and tightly coupled systems based on the extended Kalman filter and its modifications [122][123][124]. Using neural networks to simulate GNSS signals not in the field of view provides relatively good accuracy when used in integrated GNSS/INS systems [125][126][127][128].…”

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles

“…Thus it will be used together with V2X communication to cloud databases that are built by vehicles driving on the roads and sending telemetry to get actual data about the road [ 161 , 162 ]. Another way of getting the road surface information is by having detailed road profile maps saved in the vehicle’s memory and by periodically updating it This way, GNSS/INS with V2X communication enables an experience-based preview of the road surface in addition to navigation and road maps, which helps ICC to prepare for lateral accelerations on road curves, altitude changes, and decelerations and accelerations on traffic lights and intersections in advance [ 134 , 163 , 164 ]. Vehicle states such as longitudinal, lateral and vertical velocities, heading, and pitch and roll angles can be estimated by the fusion of data from various sensors and systems that overlap and therefore provide robustness and additional accuracy.…”

A robust integrated navigation optimization method for USV in signal occlusion environment

“…In response to this, some scholars have proposed optimizing the existing GNSS/INS integrated navigation filter. First of all, Adaptive Kalman filtering (AKF) [21], cubature Kalman filtering (CKF) [22] and extended Kalman filtering (EKF) [23] have been tried to replace the traditional Kalman filter for processing system state changes. Then, maximum likelihood estimation [24], variational Bayesian estimation [25], and convolutional neural networks (CNN) [26] are also used for Kalman filter optimization to dynamically approximate measurement noise.…”