计算机工程与应用2018，Vol.54Issue(11)：46-52,7.DOI:10.3778/j.issn.1002-8331.1710-0263
基于目标跟踪的自适应广义高阶CKF算法
Adaptive generalized high-degree Cubature Kalman Filter based on target tracking
摘要
Abstract
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 perfor-mance 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.关键词
非线性高斯滤波/广义高阶容积准则/自适应滤波/目标跟踪Key words
nolinear Gaussian filter/generalized high-degree cubature rule/adaptive filter/target tracking分类
信息技术与安全科学引用本文复制引用
彭志颖,夏海宝,许蕴山..基于目标跟踪的自适应广义高阶CKF算法[J].计算机工程与应用,2018,54(11):46-52,7.基金项目
航空科学基金(No.20145596025,No.20155596024). （No.20145596025,No.20155596024）
