RCKF Cooperative Navigation Algorithm for Tightly Coupled Vehicle Ad Hoc Networks Based on Huber M Estimation

As a core technology of cooperative navigation, relative position sensing is crucial for vehicle intelligent driving. It plays a key role in the cooperative positioning algorithm of vehicle ad hoc networks (VANETs). However, because of system nonlinearity and colored noise, the acquisition of a posteriori information of cooperative positioning under the same hardware platform is usually limited to a certain accuracy. To address these problems, a vehicle cooperative positioning state estimation algorithm based on a robust cubature Kalman filter (RCKF) of Huber M estimation is herein proposed. The algorithm combines the cubature rule for nonlinear updating, converts the measurement equation into a linear regression problem, and uses M estimation to solve it. The colored noise is suppressed by reducing the weight of disturbed measurements by the Huber loss function. The experimental results of tightly coupled vehicle cooperative navigation show that compared with the extended Kalman filter (EKF) and cubature Kalman filter (CKF), the positioning accuracy of the RCKF proposed in this paper is improved by 26.04% and 27.10% respectively, which effectively improves the accuracy and robustness of relative position estimation. It provides a reference system quality control strategy for vehicle cooperative positioning solutions.