摘要
当前统计模型和截断正态概率模型都需要预先设计目标最大机动加速度,不能适应各种机动情况。在截断正态概率加速度模型的基础上,提出了一种基于截断正态概率模型的模糊自适应算法。该算法使系统状态噪声方差随着机动特性能够自适应调整,自适应各种机动情况。仿真结果表明,该算法在跟踪精度和收敛速度都优于传统的基于"当前"统计模型和截断正态概率模型的跟踪算法。
The maximum accelerations of maneuvering targets must be pre-defined in the method of Current statistical（CS）model and truncation gauss probability（TGP）model. So the presented maneuvering acceleration model may be difficult to meet various maneuvering conditions. A fuzzy adaptive tracking algorithm for maneuvering target based on truncation gauss probability model is presented. This algorithm can adjust the system process noise covariance adaptively along with the characteristic of maneuvering to adapt to different target maneuvers. The simulation results show that the method is better than the conventional tracking algorithm based on Current statistical model and Truncation gauss probability model in both tracking accuracy and convergence rate.
出处
《火力与指挥控制》
CSCD
北大核心
2007年第10期62-64,67,共4页
Fire Control & Command Control
基金
国防预研基金资助项目(0301F21)
关键词
机动目标跟踪
概率模型
模糊推理
滤波
maneuvering target tracking, probability model, fuzzy inference, filter ： maneuvering target tracking,probability model, fuzzy inference, filter
