A New Adaptive High-Degree Unscented Kalman Filter with Unknown Process Noise

Vehicle state, including location and motion information, plays an essential role on the Internet of Vehicles (IoV). Accurately obtaining system state information is premise realizing precise control. However, statistics process noise are often unknown due to complex physical process. It challenging estimate when unknown. This paper proposes a new adaptive high-degree unscented Kalman filter based improved Sage–Husa algorithm. First, traditional algorithm using transform. A estimator suitable for obtained noise. Then, designed improve accuracy stability estimation system. Finally, target tracking simulation results verify proposed algorithm’s effectiveness.