Improved strong tracking Kalman filter algorithm based SINS/GNSS/ODO integrated navigation

The combination of strapdown inertial navigation system (SINS), global navigation satellite system (GNSS), and odometer (ODO) is the most practical and cost-effective way to implement a multi-source fusion automotive navigation system. However, the traditional Kalman filtering (KF) algorithm suffers from the inaccuracy of the system state matrix and the measurement noise covariance matrix during vehicle operation, which leads to a decrease in navigation and positioning accuracy. To solve this problem, a measurement adaptive strong tracking Kalman filter (MA-STKF) algorithm is proposed. The algorithm adopts an asymptotic weighting approach to estimate the measurement covariance array by considering new interest time series being actually filtered, introduces a measurement forgetting factor, perform real-time estimation and correction combines with the decay factor of the strong tracking filter, and takes advantage of the difference between the actual measurement error and the predicted covariance to reset the decay factor, which improves the tracking performance of the algorithm. The proposed algorithm is applied to the SINS/GNSS/ODO integrated navigation system, and simulation and vehicle experiments were conducted, improving the positioning longitude by 52.48% and 30.96%, and the positioning latitude by 63.27% and 37.64%, compared to KF and STKF, respectively.