IMMKF-DOA Auxiliary Vehicle Cooperative Localization Algorithm Based on Multi-base Station

The traditional Kalman filter algorithm is difficult to accurately localization the vehicle in the process of real-time motion of the vehicle. Therefore, a motion state adaptive interactive multiple model Kalman filter(IMMKF) and multiple base station direction of arrival(DOA) algorithm is proposed to estimation the real-time position of vehicle. Based on the unbiased estimator, the measurement noise covariance is updated in real time and embedded in the standard Kalman filter algorithm to realize the adaptive IMMKF. In view of the impact of different vehicle motion states and dynamic driving environments on the accuracy of vehicle positioning estimation, an adaptive IMMKF and multi-base station information fusion algorithm are constructed to estimate the vehicle position in real time. The proposed IMMKF-DOA fusion algorithm considering the change trend of vehicle positioning accuracy under different speeds of vehicle and different number of base stations, and achieves accurate estimation of vehicle real-time position. Using PreScan-Simulink union simulation platform for virtual simulation verification and real vehicle test verification. The results show that the fusion algorithm based on the IMMKF and the DOA has a higher estimation accuracy than the standard Kalman filter, which better improves the accuracy of the traditional single-model Kalman filter algorithm in the process of real-time vehicle motion state estimation. For the positioning problem, the actual vehicle test verified that the proposed algorithm has improved the accuracy of the vehicle positioning by an order of magnitude compared with the accuracy of the traditional Kalman filter algorithm and achieved more accurate vehicle position estimation. © 2023 Editorial Office of Chinese Journal of Mechanical Engineering. All rights reserved.