抗差卡尔曼滤波在惯组外场自标定中的应用

针对惯组在外场自标定受到环境的振动干扰会降低标定精度的问题,分析了粗差对卡尔曼滤波估计值的影响,提出将基于抗差估计的卡尔曼滤波算法应用于惯性测量组合(IMU)外场自标定数据处理中。该算法通过等价权函数对异常数据进行连续降权,减弱粗差对惯组输出的污染,兼具了卡尔曼滤波的实时性和等价权函数的抗差性,具有实际应用价值。实验结果表明,与Sage-Husa自适应卡尔曼滤波和抗野值卡尔曼滤波相比,抗差卡尔曼滤波具有更强的抗差性,滤波收敛速度更快,单次通电精度提高了至少1个数量级,能有效抑制异常数据对标定精度的影响。

Application of Robust Kalman Filtering to IMU Outfield Calibration

Aiming at solving the problem that the calibration accuracy of inertial measurement unit (IMU) will be affected if it is interrupted by the ground vibration when it is self calibrated in outfield, the influence of gross error on the estimated value of Kalman Filtering (KF) is analyzed firstly, then a modified KF based on Robust Estimation is proposed to be used in the data processing of IMU self calibration in outfield. By descending weights of abnormal data continuously via equivalent weight function, the influence of abnormal data on IMU outputs is minimized. Taking the advantages of KF and equivalent weight function, the new method is not only real time but also robust. Experiment results show that comparing with Sage Husa adaptive KF and Fault Tolerant KF, the robust KF is more robust, its rapidity of convergence is faster. The accuracy of single testing is improved at least one order. It can degrade the influence of abnormal data on the calibration accuracy effectively.