In order to reduce the error judgment of outliers in vehicle temperature prediction and improve the accuracy of single-station processor prediction data,a Kalman filter multi-information fusion algorithm based on opti...In order to reduce the error judgment of outliers in vehicle temperature prediction and improve the accuracy of single-station processor prediction data,a Kalman filter multi-information fusion algorithm based on optimized P-Huber weight function was proposed.The algorithm took Kalman filter(KF)as the whole frame,and established the decision threshold based on the confidence level of Chi-square distribution.At the same time,the abnormal error judgment value was constructed by Mahalanobis distance function,and the three segments of Huber weight function were formed.It could improve the accuracy of the interval judgment of outliers,and give a reasonable weight,so as to improve the tracking accuracy of the algorithm.The data values of four important locations in the vehicle obtained after optimized filtering were processed by information fusion.According to theoretical analysis,compared with Kalman filtering algorithm,the proposed algorithm could accurately track the actual temperature in the case of abnormal error,and multi-station data fusion processing could improve the overall fault tolerance of the system.The results showed that the proposed algorithm effectively reduced the interference of abnormal errors on filtering,and the synthetic value of fusion processing was more stable and critical.展开更多
针对当前利用大地水准面模型求解垂线偏差精度不高、稳健性差的问题,设计了一种严密的垂线偏差抗差最小二乘解法.首先,基于大地水准面与垂线偏差的关系,采用EGM2008(Earth Gravity Model 2008)重力场模型计算参数初始解;然后,引入中位...针对当前利用大地水准面模型求解垂线偏差精度不高、稳健性差的问题,设计了一种严密的垂线偏差抗差最小二乘解法.首先,基于大地水准面与垂线偏差的关系,采用EGM2008(Earth Gravity Model 2008)重力场模型计算参数初始解;然后,引入中位数抗差法,并选用Huber权函数计算等价权,迭代计算出稳健的垂线偏差最小二乘解;最后,结合两个实测算例对设计方法进行验证.试验结果表明,该方法计算的垂线偏差分量与约定真值最大偏差在0:5′′左右,相较于对比方法精度更高;同时,该方法能有效抵抗粗差值的影响,具有较强的稳健性.展开更多
基金supported by Natural Science Foundation of Gansu Province(No.20JR5RA407).
文摘In order to reduce the error judgment of outliers in vehicle temperature prediction and improve the accuracy of single-station processor prediction data,a Kalman filter multi-information fusion algorithm based on optimized P-Huber weight function was proposed.The algorithm took Kalman filter(KF)as the whole frame,and established the decision threshold based on the confidence level of Chi-square distribution.At the same time,the abnormal error judgment value was constructed by Mahalanobis distance function,and the three segments of Huber weight function were formed.It could improve the accuracy of the interval judgment of outliers,and give a reasonable weight,so as to improve the tracking accuracy of the algorithm.The data values of four important locations in the vehicle obtained after optimized filtering were processed by information fusion.According to theoretical analysis,compared with Kalman filtering algorithm,the proposed algorithm could accurately track the actual temperature in the case of abnormal error,and multi-station data fusion processing could improve the overall fault tolerance of the system.The results showed that the proposed algorithm effectively reduced the interference of abnormal errors on filtering,and the synthetic value of fusion processing was more stable and critical.
文摘针对当前利用大地水准面模型求解垂线偏差精度不高、稳健性差的问题,设计了一种严密的垂线偏差抗差最小二乘解法.首先,基于大地水准面与垂线偏差的关系,采用EGM2008(Earth Gravity Model 2008)重力场模型计算参数初始解;然后,引入中位数抗差法,并选用Huber权函数计算等价权,迭代计算出稳健的垂线偏差最小二乘解;最后,结合两个实测算例对设计方法进行验证.试验结果表明,该方法计算的垂线偏差分量与约定真值最大偏差在0:5′′左右,相较于对比方法精度更高;同时,该方法能有效抵抗粗差值的影响,具有较强的稳健性.
