针对噪声分布未知情况下的非线性目标跟踪问题, 提出了基于Sage-Husa算法的自适应嵌入式容积卡尔曼滤波算法。首先利用嵌入式容积准则改进传统的Sage-Husa算法, 得到适用于嵌入式容积卡尔曼滤波器的噪声统计估计器来估计未知噪声的统计特性, 并实现对其修正, 引入判断机制来抑制目标跟踪时的发散问题, 最后通过机动目标跟踪仿真验证了该算法的有效性。

A new adaptive embedded Cubature Kalman Filter (CKF) algorithm based on the Sage-Husa algorithm is proposed for nonlinear target tracking when the distribution of noise is unknown. Firstly, the embedded cubature guidelines are used to improve the conventional Sage-Husa algorithm, thus a noise statistical estimator adaptive to the embedded CKF is obtained for estimating the statistical characteristics of the unknown noise and making correction. Then, a judgment mechanism is introduced into the algorithm to suppress the divergence of the filter. Finally, the result of the maneuvering target tracking simulation verifies the effectiveness of the proposed algorithm.