摘要
分析了“当前”统计模型机动目标跟踪算法的性能对目标机动加速度最大值的依赖性,但是由于在实际中目标机动加速度的最大值往往是未知或不能准确已知的,所以为了克服“当前”统计模型的这一不足之处,采用协方差匹配和多级白噪声自适应滤波算法的思想,提出了一种“当前”统计模型在最大加速度未知情况下的机动目标跟踪新算法。对三种典型的机动目标运动形式进行了Monte-Carlo仿真研究,结果表明新算法对于解决机动目标跟踪问题非常有效。
It is analyzed that performance of the'current 'statistical model depends on maximum accelerations of the maneuvering targets,but in reality,these maximum accelerations are always unknown or known inaccurately,so in order to overcome this shortcoming,by using the idea of covariance matching and multiple level white noise adaptive filtering algorithms ,a new maneuvering target tracking algorithm using'current 'statistical model with unknown maximum accelerations is proposed.By means of Monte-Carlo simulations in three typical maneuvering target tracking scenarios,validity of this new algorithm to solve maneuvering target tracking problems has been proved.
出处
《计算机工程与应用》
CSCD
北大核心
2003年第29期19-22,共4页
Computer Engineering and Applications
基金
国家973重点基础研究发展规划项目(编号:2001CB309403)
关键词
机动目标跟踪
自适应滤波
“当前”统计模型
最大加速度
maneuvering target tracking,adaptive filtering,'current 'statistical model,maximum acceleration
