FAN Ze-yuan1,2， DONG Yu1,2
（1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China；2. Rail Transit Electrical Automation Engineering Laboratory of Gansu Province,
Lanzhou Jiaotong University, Lanzhou 730070, China）
Abstract： The measurement accuracy of speed and distance in high-speed train directly affects the control precision and driving efficiency of train control system. To improve the capability of train self-control, a combined speed measurement and positioning method based on speed sensor and radar which is assisted by global positioning system(GPS) is proposed to improve the accuracy of measurement and reduce the dependence on the ground equipment. In consideration of the fact that the filtering precision of Kalman filter will decrease when the statistical characteristics are changing, this paper uses fuzzy comprehensive evaluation method to evaluate the sub-filter, and information distribution coefficients are dynamically adjusted according to filtering reliability, which can improve the fusion accuracy and fault tolerance of the system. The sub-filter is required to carry on the covariance shaping adaptive filtering when it is in the suboptimal state. The adjustment factor of error covariance is obtained according to the minimized cost function, which can improve the matching degree between the measured residual variance and the system recursive residual. The simulation results show that the improved filter algorithm can track the changes of the system effectively, enhance the filtering accuracy significantly, and improve the measurement accuracies of train speed and distance.
Key words： information fusion; federated Kalman filter; fuzzy comprehensive evaluation; train speed and distance measurements
CLD number： TP873Document code： A
Article ID： 1674-8042（2018）03-0286-07doi： 10.3969/j.issn.1674-8042.2018.03.011
References
［1］Petr E, Roman M, Libor P. Train locator using inertial sensors and odometer. In: Proceedings of IEEE Intelligent Vehicles Symposium, Italy, 2004: 860-865.
［2］Tian T, Sun S, Li N. Multi-sensor information fusion estimators for stochastic uncertain systems with correlated noises. Information Fusion, 2016, 27(C): 126-137.
［3］Kim K, Seol S, Kong S H. High-speed train navigation system based on multi-sensor data fusion and map matching algorithm. International Journal of Control, Automation and Systems, 2015, 13(3): 503-512.
［4］Wang G, Li S, Fang Y G, et al. Research on improving the accuracy of speed and distance measurement of train by redundant convolution fault-tolerant analysis and multiple data fusion. Journal of Applied Science, 2013, 13(12): 2300-2305.
［5］Wu X H, Tao H Q, Cai X. Vehicle wheel diameter calibration method based on multi-sensor information fusion in rail transit. Urban Mass Transit, 2015, 18(6): 21-27.
［6］Yan J P, Chen X Q, Hou T. Train speed measurement information fusion based on the improved federated Kalman filter algorithm. Journal of Railway Science and Engineering, 2012, 9(2): 89-93.
［7］Chingiz H, Halil E S. Robust adaptive Kalman filter for estimation of UAV dynamics in the presence of sensor/actuator faults. Aerospace Science and Technology, 2013, 28(1): 376-383.
［8］Li H W. The research of radar rpeed measurement sensor and its test system. Wuhan: Huazhong University of Science and Technology, 2013: 9-19.
［9］Wang X L, Wang Z J, Ji S Y, et al. Single station velocity estimation: GPS vs BDS. Journal of Navigation and Positioing, 2015, 3(3): 38-42+55.
［10］Hou T. Speed control study of multi-mode intelligent control based on multi-information on fusion and filter on high-speed train. Lanzhou: Lanzhou Jiaotong University, 2015: 30-59.
［11］Carlson N A. Federated filter for fault-tolerant integrated navigation systems. In: Proceedings of IEEE Position Location & Navigation Symposium, Orlando FL, 1988: 110-119.
［12］Wan J R, Pei W, Chen X. Method of adaptive information fusion based on the fuzzy assessment theory for integrated navigation system. Systems Engineering and Electronics, 2005, 27(2): 308-311.
［13］Hu B Q. Foundations of fuzzy theory. Wuhan: Wuhan University Press, 2010: 254-272.
［14］Liu B, Mu R J, Zhao H, et al. In-motion alignment adaptive filter method for GNSS-aided strap-down inertial navigation system. Journal of Chinese Inertial Technology, 2016, 24(5): 577-582.
［15］Li R. Study on the application of Kalman filtering in the intergrated navigation. Electronic Measurement Technology, 2017, 40(3): 158-162.
基于模糊自适应联合卡尔曼滤波算法的列车测速测距信息融合
樊泽园1,2， 董昱1,2
（1. 兰州交通大学 自动化与电气工程学院， 甘肃 兰州 730070； 2. 甘肃省轨道交通电气自动化工程实验室（兰州交通大学）， 甘肃 兰州 730070）
摘要: 高速列车的测速定位精度直接影响着列控系统的控制精度和行车效率， 为了提高列车自主控车能力， 本文以GPS辅助速度传感器、 雷达进行组合测速定位， 以提高测速定位精度并减少对地面设备的依赖。 针对卡尔曼滤波在统计特征变化时滤波精度下降的问题， 运用模糊综合评判方法对子滤波器进行评价， 根据滤波置信度动态调整信息分配系数， 提高该系统的融合精度和容错性； 当子滤波器处于次优状态则进行协方差成形自适应滤波， 依据最小化代价函数获得误差协方差的调节因子， 来提高实测残差方差和系统递推残差的匹配度， 增强滤波精度。 仿真结果表明， 本文提出的改进滤波算法能够有效跟踪系统变化情况、 明显增强滤波精度及提高测速定位精度。
关键词:信息融合； 联合卡尔曼滤波； 模糊综合评判； 列车测速测距
引用格式：FAN Ze-yuan， DONG Yu. Information fusion of train speed and distance measurements based on fuzzy adaptive Kalman filter algorithm. Journal of Measurement Science and Instrumentation, 2018, 9(3)： 286-292. ［doi：10.3969／j.issn.1674-8042.2018.03.011］
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