融合深度学习与多模型滤波的无人车协同导航方法

In order to improve the accuracy and robustness of cooperative navigation in complex environments, an unmanned vehicle cooperative navigation method integrating deep learning and multi-model filtering is proposed. The deep learning network is deeply integrated with the interactive multiple model (IMM) prediction algorithm and incorporated into the design of the cooperative navigation system. Efficient data-level integration and complementarity have been achieved, significantly enhancing the adaptability and accuracy of the navigation system in complex and highly dynamic environments. To validate the effectiveness of the proposed algorithm, real-vehicle tests are conducted in complex environments, where the maximum error of the cooperative navigation system is merely 0.3 m over a 200 m test path, which is increased by 27.9% compared with the original laser/inertial cooperative navigation method. This result confirms the significant advantages and engineering practical value of the proposed method in the cooperative navigation system under satellite rejection environments, providing robust technical support for future autonomous navigation of intelligent unmanned systems under extreme conditions.