A Smoothing Algorithm Based on Adaptive RTSS for GNSS/IMU Tightly Coupled Navigation and Positioning

In the tightly coupled navigation and positioning technology of the Global Navigation Satellite System (GNSS) and Inertial Navigation System (IMU), data fusion can effectively improve positioning accuracy and system reliability. The current tightly coupled system still faces some challenges, such as the decrease in positioning accuracy caused by the accumulation of low-cost IMU errors and the loss of GNSS signals in obstructed environments. Aiming at these problems, this article proposes an adaptive recursive Rauch-Tung-Striebel Smoother (AR-RTSS) algorithm to address these issues. This algorithm achieves dynamic optimization of the traditional RTSS algorithm by introducing adaptive factors and recursive least squares estimation. In addition, a new nonlinear system processing method has been proposed, which extends the applicability of the RTSS algorithm through second-order state equation approximation and improved measurement update models. The experimental results show that the proposed AR-RTSS algorithm has higher positioning accuracy than traditional smoothing algorithms, and can also achieve good positioning and attitude performance in environments where GNSS signals are obstructed.