摘要
在直角坐标下,目标的运动模型能较好地描述,一般认为能得到更高的滤波精度。文中对直角坐标系下的修正无偏转换测量卡尔曼滤波(MUCMKF)、扩展卡尔曼滤波(EKF)、无迹滤波(UF)算法进行了总结分析,并对它们的初始协方差矩阵设置给出了建议,以使滤波器快速收敛。最后,通过实验,对几种方法的性能进行了比较。结果表明,在较大的测量误差下,UF、MUCMKF的性能明显好于EKF。
It＇s generally accepted that tracking in cartesian coordinates is more accurate because the object motions are easier described in cartesian coordinate than in spherical coordinate.Therefore,three cartesian coordinate algorithms which named modified unbiased converted measurement Kalman filter（MUCMKF）,extended Kalman filter（EKF）,and unscented filter（UF） respectively are investigated together.Additionally,the initialization value of estimated covariance matrixes is proposed to ensure the filters convergence quickly.Finally,experiments are carried out to evaluate the algorithms＇ performance and results indicate that UF and MUCMKF have distinct superiority over EKF when measurement error is high.
出处
《现代雷达》
CSCD
北大核心
2010年第10期33-36,共4页
Modern Radar
关键词
修正无偏转换测量卡尔曼滤波
扩展卡尔曼滤波
无迹滤波
modified unbiased converted measurement kalman filter
extended kalman filter
unscented filter
