基于“当前”统计模型的自适应不敏卡尔曼滤波算法  

Adaptive Unscented Kalman Filtering Algorithm Based on Current Statistical Model

作　　者：简金蕾[1] 李静[1] 任宏斌[1] 郭颖睿[1] 

机构地区：[1]空军工程大学导弹学院,陕西三原713800

出　　处：《现代雷达》2011年第9期48-52,共5页Modern Radar

摘　　要：基于机动目标"当前"统计模型在直角坐标系下建立了三坐标雷达跟踪系统的状态方程和观测方程。针对非线性自适应滤波这一问题,提出了一种基于"当前"统计模型的自适应不敏卡尔曼滤波算法(CS-UKF),并对算法作了说明。通过计算机仿真验证了CS-UKF算法的有效性,并且该算法跟踪效果良好,精度好于基于"当前"统计模型的自适应扩展卡尔曼滤波算法(CS-EKF)算法。State equation and observation equation of three dimensional radar tracking system are built based on current statistical model. In order to solve the problem about nonlinear adaptive filtering, a novel algorithm is presented and explained. The validity of the new algorithm is proved by the simulated results and the results also indicate that CS-UKF whose tracking precision is better than CS-EKF can track target very well.

关 键 词：控制理论 机动目标跟踪 “当前”统计模型 不敏卡尔曼滤波 

分 类 号：TN953[电子电信—信号与信息处理]