Multi-information fusion algorithm for temperature prediction based on MP-Huber Kalman filter

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.