基于改进联合卡尔曼滤波算法的列车测速信息融合

基于高速列车的测速精度直接影响车载设备的控制精度,为了提高测速精度,在分析基于联合卡尔曼滤波算法的多路测速传感器信息融合原理、结构和算法的基础上,针对联合卡尔曼滤波系统因缺乏系统噪声和测量噪声的先验知识而导致滤波精度下降的问题,运用自适应联合卡尔曼滤波思想,对系统过程噪声和测量噪声的统计特性实时进行估计和修正,并将改进前后的算法进行计算机仿真。研究结果表明:改进后的滤波算法具有更好的融合精度,更稳定的滤波效果,能够进一步提高测速系统对环境的适应能力。

Train speed measurement information fusion based on the improved federated Kalman filter algorithm

Speed measurement accuracy of high-speed train directly affects the control accuracy of vehicle equipment.In order to improve the measurement precision,the principle,structure and algorithm of multi speed sensor measurement information fusion were analyzed based on federated Kalman filter.Aimed at the problem,due to lack of process noise and measurement noise prior knowledge,filtering precision decline,the adaptive federated Kalman filter was used.The statistic characteristics of process noise and measurement noise can be real-time estimated and corrected.by simulated original and improved algorithms,the results show that the improved algorithm has a better fusion accuracy and more stable filtering effect,and can further improve the adaptive capacity of measurement system to the environment.