Research on Integrated Navigation Systems for High-Speed Vehicles

Research on integrated navigation for high-speed vehicles involves utilizing multiple navigation information sources, such as GNSS, IMU, and map data, to enhance the positioning accuracy and robustness of vehicle navigation systems. In this field, Kalman Filter (KF) and Extended Kalman Filter (EKF) are two commonly used algorithms. The Kalman Filter is a recursive algorithm suitable for linear systems, typically implemented through prediction and correction steps where the state vector and covariance matrix are updated recursively. The Extended Kalman Filter extends this approach to nonlinear systems by linearizing the system model using Taylor series expansion, making it applicable to a wider range of practical scenarios through Jacobian matrix calculations. Beyond these two algorithms, many other navigation algorithms have found extensive application in practice, including Particle Filter (PF), Unscented Kalman Filter (UKF), and more recently popular deep learning approaches. Therefore, in the field of high-speed vehicle integrated navigation research, algorithm selection and optimization represent a critical research direction that can significantly impact the performance and reliability of navigation systems.