基于目标跟踪的自适应广义高阶CKF算法

针对容积卡尔曼滤波在系统状态突变时滤波精度下降的问题，结合广义高阶容积卡尔曼滤波和强跟踪滤波算法，提出了一种自适应广义高阶容积卡尔曼滤波（AGHCKF）方法。采用广义高阶容积准则和矩阵对角化变换，以提高算法的滤波精度和稳定性。引入强跟踪滤波，利用渐消因子在线修正预测误差协方差阵，强迫残差序列正交，以增强算法应对系统状态突变等不确定因素的能力。将提出的AGHCKF算法应用于带有未知状态突变的机动目标跟踪问题并进行数值仿真，结果表明，AGHCKF在系统状态突变时能保证较高的滤波精度，具有较强的鲁棒性和系统自适应能力。

Adaptive generalized high-degree Cubature Kalman Filter based on target tracking

In order to overcome the problem that Cubature Kalman Filter decreases in accuracy when system states suddenly change, combined with the generalized high-degree Cubature Kalman Filter and strong tracking filter algorithm, an Adaptive Generalized High-degree Cubature Kalman Filter（AGHCKF） is established. The generalized high-degree cubature rule and the diagonalization of matrix are used to improve the accuracy and the stability of filtering algorithm. Furthermore, the strong tracking filter algorithm is introduced to improve the capability of the filter to deal with uncertainty factors by modifying the predicted states’ error covariance with a fading factor and the residual sequence is forced to be orthogonal. A maneuvering target tracking problem with unknown sudden states changes in system states is used to test the performance of AGHCKF. The simulation results indicate that AGHCKF can achieve good filtering performance when states’ changes suddenly occur, with great robustness and better system adaptive capacity.