摘要
研究在距离和径向速度测量噪声统计相关的情形下把径向速度测量引入Kalman滤波的新方法。分析了径向速度测量噪声与位置测量更新后状态滤波误差的统计相关性,根据Gauss-Markov定理导出了对应于径向速度测量的滤波方程由此而得到一种序贯滤波算法。两个不同的蒙特卡罗仿真表明,通过采用这一新算法引人径向速度测量,不仅可以大大提高状态估计的精度,而且其估计性能和计算效率优于传统的EKF。
A new algorithm is developed to incorporate the radial velocity measurement into Kalman filter in the case of correlation between range and radial velocity measurement noises. An analysis is given about statistical correlation between the radial velocity measurement noise and filtering errors after position measurements updating. The filtering equations for radial velocity measurement are derived from Gauss-Markov theorem and therefore a sequential filter is obtained. Two different Monte Carlo simulations show that the new algorithm cannot only improve state estimation accuracy but also is superior to EKF in estimation performance and computation efficiency.
出处
《信号处理》
CSCD
2002年第5期414-416,409,共4页
Journal of Signal Processing
