摘要
传统卡尔曼滤波器依赖目标运动状态的数学模型,当目标运动数学模型不精确或不能够用线性状态空间模型描述时,跟踪滤波会发散。针对这一问题,提出了一种基于GM(1,1)(Grey model)模型的跟踪卡尔曼滤波方法。在卡尔曼滤波过程中,迭代所需的预测值不再依赖所建立的目标运动状态方程,而是用前几个时刻的估计值建立灰色微分方程来预测下一时刻的值,其预测精度高,滤波性能提高,特别在目标机动的时间内跟踪滤波效果要好于传统方法。仿真结果表明,是一种可行的机动目标跟踪方法。
The estimate accuracy of traditional Kalman filter depends on the state model of targets. When the state of targets would not be described by a precise mathematics model or a line state space model, the result will be volatilization. The paper provides a new algorithm by introducing GM（1,1） into Kalman filter. In course of Kalman filter, the forecasting value depends on the state model of targets no longer. The next value is forecasted by using a few forward estimated values with a grey differential equation. Not only its accuracy is higher, hut also it＇s performance is better. Especially, during target maneuvering, the result of the filter is better than that of the traditional method. Experiment results indicate that the way of maneuvering target tracking is feasible.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2009年第6期1396-1399,共4页
Systems Engineering and Electronics
基金
国家高技术研究发展计划(863项目)资助课题(2007AA809502B)
