中文摘要：

针对非线性系统中测量噪声统计特性未知导致滤波精度不高或发散的问题，提出了一种自适应平方根高阶容积卡尔曼滤波（high—degreecubatureKalmanfilter，HCKF）算法。采用QR分解、Cholesky因子更新和高效最小二乘法等矩阵分解技术设计了一种平方根HCKF算法（SHCKF），提高了滤波算法的数值稳定性，减小了状态估计误差。引入Sage．Husa估计器在线估计未知测量噪声的方差，进一步提高了SHCKF的估计精度，扩大了算法的应用范围。通过几个计算机仿真实验表明，与标准的HCKF算法相比，新算法具有更好的估计精度，尤其是在测量噪声统计特性不确定的场景下。

英文摘要：

Aiming at low filtering precision and divergence caused by unknown measurement noise statistics in nonlinear sys- tem, this paper proposed an adaptive square-root high-degree cubature Kalman filter （HCKF）. First of a11, it introduced matrix factorization techniques including QR decomposition, Cholesky factor updating and efficient least squares to design a square- root HCKF. Therefore,it effectively improved the filter numerical stability and reduced the state estimation error. Secondly, it used Sage-Husa estimator to online estimate the unknown measurement noise covariance,which further improved the state esti- mation accuracy and enlarged its application scope. Finally, the simulations show that the proposed methods can provide better performance in estimation accuracy than the standard HCKF, especially in the case of uncertain measurement noise statistics.