基于自适应卡尔曼滤波的动态称重算法的研究

Research of Dynamic Weighing Algorithm Based on Adaptive Kalman Filter

在使用卡尔曼滤波算法对动态称重数据进行滤波时,一般假设系统的量测噪声为常量。在实际应用中,由于车辆自身结构和过车状态的差异,实际产生的量测噪声是随机变量。为了减少时变干扰噪声对系统状态估计的影响,在滤波算法中通过最小二乘法加入干扰噪声调节器,在线估计噪声的特性实现自适应滤波。在实际使用中证实,该改进后的算法不仅能有效防止滤波发散,还克服了车辆振动、路面不平和车辆拖磅等因素对称量结果的影响,使系统称量误差小于2%,称量准确度等级达到2级指标。

It is usually supposed that the measurement noises were lots of constants,when the Kalman filter algorithm was used to filter the dynamic weighing data,however,in practical applications the measurement noise were randomly varying because the differences of structure of vehicles itself and the states. To reduce the time-varying noises affects ,adding noise regulator to Kalman algorithm by using maximum likelihood least square algorithm,estimating noises online to achieve the adaptive Kalman filter. It was proved in practical use that the improved algorithm can effectively prevent the divergence of filtering,and overcome the impacts caused by vehicle vibration,uneven pavement, axis-dragging and so on. As a result,the system weighing error was less than 2% and the weighing accuracy attained the second class target.