中文摘要：

在目标跟踪中,噪声的统计特性未知可能会引起滤波精度下降甚至发散,针对该问题,提出了一种新的自适应平方根容积卡尔曼滤波算法。所提方法在常规Sage-Husa算法的基础上采用容积规则,推导出了一种适用于非线性系统的自适应噪声统计估计器。仿真结果显示,相对于标准的平方根容积卡尔曼,所提方法在噪声统计特性未知或时变的情况下滤波精度有显著提高。

英文摘要：

To solve the problems of low precision and divergence of filter caused by unknown system noise statistics in target tracking, a new adaptive square cubature Kalman filter （CKF） algorithm is proposed. A noise statistics estimator designed for nonlinear systems is derived by applying the cubature rule based on the Sage-Husa algorithm. Simulation results show that as compared with the standard square CKF algorithm, the proposed algorithm provides higher accuracy when the system noise statistics is unknown or time-varying.