基于序列批处理Kalman滤波的跟踪算法

Target tracking technology based on sequential block Kalman filter

摘要:

提高跟踪准确度是雷达发展的重要方向之一，本文通过建立的雷达跟踪模型，从批处理的角度，将多个状态矢量联合进行处理，并改进了量测方程，给出了一种使用序列批处理Kalman滤波(SBKF)以提高雷达跟踪准确度的新手段。通过仿真实验看出，相比传统扩展卡尔曼滤波(EKF)算法，序列批处理Kalman滤波的结果更接近真实值，有更好的收敛性，能得到更加稳定的滤波结果，有效地抑制了量测方程非线性化和野值带来的影响。

Abstract:

In radar system, one of the most important direction of research is how to improve the accuracy of target tracking. In this study, a new algorithm, Sequential Block Kalman Filter(SBKF), was proposed based on the traditional radar tracking model, from the perspective of batch process. This algorithm jointed different state vectors together and modified the measurement equation to improve the track accuracy. It is proved by simulation that SBKF is superior in filtering precision and convergence to traditional Extended Kalman Filter(EKF). This new algorithm can obtain more stable results, and can also suppress the effect of nonlinear measurement equation and the wild value.