摘要
在实际的目标跟踪过程中,由于目标远近等各种客观因素的影响,观测噪声是随时变化的。但是在标准卡尔曼滤波中,如果将观测噪声协方差设为恒定值,必然造成跟踪结果不理想。针对这种情况,通过在任意时刻施行两次卡尔曼滤波的结果来自适应地调整观测噪声协方差,使卡尔曼滤波算法中的观测噪声协方差与实际值更加接近,从而提高对目标的跟踪精度。最后Monte Carlo仿真实验证明了本算法的有效性。
In the actual course of target tracking,the observation noise is changing at any time because of the impact of the distance between the target and the radar and other reasons.However,the observation noise covariance is changeless in the normal Kalman filter,so the tracking result is not perfect inevitably.In order to solve this problem,a novel algorithm is presented to adjust the observation noise covariance adaptively based on the two results of Kalman filter with different sample times.The simulation experiments show that the proposed algorithm improves the result of tracking.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2010年第2期232-234,共3页
Systems Engineering and Electronics
关键词
目标跟踪
卡尔曼滤波
观测噪声
自适应跟踪算法
target tracking
Kalman filter
observation noise
adaptive tracking algorithm
