磁浮管道物流系统智能测速算法研究

Research on intelligent velocity measurement algorithm of maglev pipeline logistics system

测速定位技术是轨道交通系统安全防护、牵引优化和运行组织的重要基础。针对磁浮管道物流运输系统多普勒雷达测速定位模块在复杂工况中量测噪声统计特性变化及数据失常的问题,提出一种新型Sage-Husa自适应滤波算法。由于Sage-Husa自适应滤波算法存在计算量大、易发散等问题,在其基础上进一步改进,引入模糊推理系统,以残差协方差的实际值与理论值之比作为模糊推理系统的输入,输出调节因子对量测噪声协方差进行实时修正。设置失常数据判断阈值,通过调节卡尔曼增益实现对失常数据的补偿,有效地提高了对磁浮管道物流运输系统的速度估计精度。仿真实验结果表明,在测速数据正常的情况下,相比于传统卡尔曼滤波器、Sage-Husa自适应滤波器,新型Sage-Husa自适应滤波器精度分别提高10.88%和4.97%;在测速数据失常的情况下,相较于前2种卡尔曼滤波器,新型Sage-Husa自适应滤波器能够更好地剔除失常数据的影响,且能够较快地收敛到真实值附近。最后通过实测车载数据对新型Sage-Husa自适应滤波算法进一步验证,并与传统的卡尔曼滤波算法以及Sage-Husa自适应滤波算法对比,其算法精度分别提高10.3%和3.6%。新型Sage-Husa自适应滤波算法对随机量测噪声抑制能力更强,速度估计精度更高,并具有对失常数据的补偿能力,能够适应管轨车辆测速定位的场景需求,为管道物流系统乃至其他轨道交通系统的安全运行提供可靠保障。

Speed measurement and positioning technology is an important basis for safety protection,traction optimization and operation organization of rail transit systems.A new Sage-Husa adaptive filtering algorithm was proposed to solve the problem of measuring noise statistical characteristic changes and data aberrations in Doppler radar velocity measurement and positioning modules in maglev pipeline logistics transportation systems under complex working conditions.Since the Sage-Husa adaptive filtering algorithm has some problems,such as large computation amount and easy dispersion,this paper further improved it by introducing a fuzzy reasoning system,using the ratio of the actual value of the residual covariance to the theoretical value as the input for the fuzzy inference system and the output adjustment factor for real-time correction to the measured noise covariance;by setting a judgment threshold of abnormal data and compensating for these abnormal data by adjusting Kalman gain,the speed estimation accuracy of maglev pipeline logistics transportation system was improved effectively.The simulation results show that in the case of normal velocity data,compared with traditional Kalman filter and Sage-Husa adaptive filter,the accuracy of the new Sage-Husa adaptive filter is respectively improved by 10.88%and 4.97%;in the case of anomalous velocity data,compared with the first two Kalman filters,the new Sage-Husa adaptive filter is able to remove the influence of the anomalous data betterly and converge to the true value more quickly.Finally,the new Sage-Husa adaptive filtering algorithm is further verified by the measured on-board data,and compared with the traditional Kalman filtering algorithm and Sage-Husa adaptive filtering algorithm,the algorithm accuracy is improved by 10.3%and 3.6%,respectively.The new Sage-Husa adaptive filtering algorithm has stronger suppression ability of random measurement noise,higher speed estimation accuracy,and compensation ability for abnormal data.It can adapt to the scene requirements of speed measurement and positioning in tube rail vehicles,providing reliable guarantee for the safe operation of pipeline logistics systems and other rail transit systems.