Tightly Coupled GNSS/INS Integration with Robust Sequential Kalman Filter for Accurate Vehicular Navigation

Abstract

With the development of multi-constellation multi-frequency Global Navigation Satellite Systems (GNSS), more and more observations are available for tightly coupled GNSS/Inertial Navigation System (INS) integration. Concerning the accuracy, robustness, and computational burden issues in the integration, we proposed a robust and computationally efficient implementation. The new tight integration model uses pseudorange, Doppler and carrier phase simultaneously, to achieve the maximum possible navigation accuracy for a single receiver. The resultant high-dimensional observation vector is then processed by a sequential Kalman Filter (KF) to improve the computational efficiency in the measurement update step. Based on the innovation of the sequential KF, a robust estimation method with Gaussian test is further devised to detect and adapt the faults in individual GNSS channels. Two field vehicular tests are conducted to evaluate the performance improvements of the proposed method, compared with loose coupling and conventional tight coupling. Test results in favorable environments indicate that the proposed method can significantly improve the velocity and attitude accuracy by 69.42% and 47.16% over loose coupling and by 64.75% and 30.88% over conventional tight coupling, respectively. Moreover, the computational efficiency is also improved by about 53.09% for the proposed method, compared with batch KF processing. In GNSS challenging environments, the proposed method also shows superiority in terms of velocity and attitude accuracy, and better bridging capability during the GNSS partial or complete outages. These results demonstrate that the proposed method is able to provide a more robust and accurate solution in real-time vehicular navigation.

Fast Motion Model of Road Vehicles with Artificial Neural Networks

“…The maximal position error remains under 1.5 m, and the maximal yaw angle error does not exceed 4 • for the complete test dataset. These results are not much worse than the guaranteed performance of fused GNSS (Global Navigation Satellite System), and INS (Inertial Navigation System) localization solutions for global positioning [37]. Naturally, the most significant deviations can be observed in the case of the scenarios that are the most dynamically demanding with the largest longitudinal and lateral accelerations.…”

A Novel Two-Stage Adaptive Filtering Model-Based GNSS/INS Tightly Coupled Precise Relative Navigation Algorithm for Autonomous Aerial Refueling

“…The system state-space model depends on the INS error model and system error description of inertial sensors. The integrated system model can be described as the following psi-angle error equations [32] without considering satellite system errors:…”

High-Altitude Balloon-Based Sensor System Design and Implementation

“…Some other systems such as GNSS receivers or odometer provide low-frequency measurements that can be fooled by jamming or short-term measurement errors, but these sensors provide good performance over the long term. The basic idea of Extended Kalman Filter (EKF) is to take the best of each sensor, and it includes a high-frequency prediction step using inertial sensors to precisely measure motion and navigation data [14][15][16]. The loose coupling between GPS/GNSS and the EKF allows GPS data to improve inertial sensor performance, and on the other hand, inertial data improve overall navigation performance.…”