文章基本信息

标题：Robust adaptive UKF based on SVR for inertial based integrated navigation
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作者：Meng-de Zhang ; Hai-fa Dai ; Bai-qing Hu 等
期刊名称：Defence Technology
印刷版ISSN：2214-9147
出版年度：2019
页码：1-10
DOI：10.1016/j.dt.2019.10.012
出版社：Elsevier B.V.
摘要：Aiming at the problem that the traditional Unscented Kalman Filtering (UKF) algorithm can't solve the problem that the measurement covariance matrix is unknown and the measured value contains outliers, this paper proposes a robust adaptive UKF algorithm based on Support Vector Regression (SVR). The algorithm combines the advantages of support vector regression with small samples, nonlinear learning ability and online estimation capability of adaptive algorithm based on innovation. Firstly, the SVR model is trained by using the innovation in the sliding window, and the new innovation is monitored. If the deviation between the estimated innovation and the measured innovation exceeds a given threshold, then measured innovation will be replaced by the predicted innovation, and then the processed innovation is used to calculate the measurement noise covariance matrix using the adaptive estimation algorithm. Simulation experiments and measured data experiments show that SVRUKF is significantly better than the traditional UKF, robust UKF and adaptive UKF algorithms for the case where the covariance matrix is unknown and the measured values have outliers.
关键词：Integrated navigation ; Support vector regression ; Unscented Kalman filter ; Robust filter ; Adaptive filter