关键词: 非线性自适应平方根无迹卡尔曼滤波方法（NASRUKF）, 卡尔曼滤波, 平方根无迹卡尔曼滤波（SRUKF）, Sage-Husa滤波, 非线性滤波, 预估

Abstract: In this paper, a Nonlinear Adaptive Square-Root Unsented Kalman Filtering（NASRUKF） approach is described for nonlinear systems with additive noise which have unknown statistical characteristics. Based on the square-root algorithm, the traditional Sage-Husa adaptive filter’s estimator is modified and combinated with the Square Root Unscented Kalman Filtering（SRUKF） for nonlinear filtering. The process noise covariance matrix Q or the measurement noise covariance matrix R is estimated straightforwardly in proposed NASRUKF. Thus, the positive semidefiniteness and symmetrical properties of the filter are improved. Simulation results show that NASRUKF performs better than SRUKF in the aspects of the accuracy, stability and self-adaptability.

Key words: Nonlinear Adaptive Square-Root Unsented Kalman Filtering（NASRUKF）, Kalman filtering, Square Root Unscented Kalman Filtering（SRUKF）, Sage-Husa filtering, nonlinear filtering, estimating