The adaptive unscented Kalman filter (AUKF) is usually used to estimate the three main parameters of vehicle active safety control: yaw rate, sideslip angle, and longitudinal speed. However, abnormal results often occur during the data collection and transmission of the sensor, and the algorithm is also affected by Gaussian noise, which in turn affects the accuracy of the state estimation. The proposed multi-innovation adaptive robust unscented Kalman filter (QS-MIARUKF) algorithm using Qatar Riyal (QR) decomposition and Singular Value Decomposition (SVD) reduces the effect of gross error and Gaussian noise on state estimation, increases robustness of the algorithm and improves accuracy of the estimation algorithm. Using the IGGIII equivalent weight function in M-estimation, the data are split into three regions: normal, weighted, and eliminated. The effect of outliers is reduced by weighting or eliminating abnormal data. QS decomposition (QR decomposition and SVD) is applied to ensure positive definiteness of the matrix of state error covariance, solve the numerical sensitivity problem in the measurement update process, and increase the algorithm robustness. The multi-innovation theory and adaptive sliding window strategy are combined with QS-ARUKF to make full use of historical data and increase its accuracy. The results reveal that the QS-MIARUKF algorithm exhibits superior robustness, convergence, and accuracy than the existing state estimation algorithm.
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