摘要
目标运动状态的改变将导致目标跟踪算法精度降低或发散。为了提高机动目标跟踪的跟踪性能,首先,针对当前统计(current statistical,CS)模型中最大加速度固定设置导致模型误差增大的问题,提出了一种自适应CS模型;在自适应CS模型和交互式多模型(interacting multiple model,IMM)的基础上,提出了一种交互式多自适应模型(interacting multiple adaptive model,IMAM),该模型通过采用两个自适应CS模型,能够有效消除目标状态突变造成模型误差急速增大的问题,提高了模型的准确度和适应性。其次,在IMAM的基础上,结合修正卡尔曼滤波(amendatory Kalman filter,AKF)的思想,提出了IMAM-AKF算法,该算法通过修正最终的状态融合估计值,有效地降低了目标机动造成的模型误差,进一步提高了机动目标跟踪的性能。最后,结合自适应渐消卡尔曼滤波(adaptive fading Kalman filter,AFKF)的思想,提出了IMAM-AFAKF算法。仿真结果表明,无论是强机动还是弱机动,IMAM-AFAKF算法都具有较好的跟踪性能。
The change of target motion will lead to a low precision or divergence of the tracking algorithm. In order to improve the performance of the maneuvering target tracking, three approaches are proposed as fol- lows. Firstly, aiming at the increase of the model error resulted by the fixed maximum acceleration in the cur- rent statistical （CS） model, an adaptive CS model is proposed. And then, on the basis of the adaptive CS model and the interacting multiple model （IMM）, a new interacting multiple adaptive model （IMAM） is proposed. Adopting two adaptive CS models, the IMAM can effectively get rid of the rapid increase of the model error caused by the abrupt change of target motion and improve the accuracy and adaptability of the model. Secondly, based on the IMAM and amendatory Kalman filter （AKF）, an IMAM-AKF algorithm is proposed. By amen- ding the estimate of state fusion in the IMAM, the IMAM-AKF greatly decreases the model error and improves the performance of the maneuvering target tracking. Finally, combining the adaptive fading Kalman filter （AFKF）, the IMAM-AFAKF algorithm is proposed. The simulation results indicate that the IMAM-AFAKF has a better performance both in high and weak maneuvers.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2016年第5期977-983,共7页
Systems Engineering and Electronics
基金
航空科学基金(20145596025)资助课题
关键词
机动目标跟踪
目标运动状态改变
模型误差
当前统计模型
交互式多模型
maneuvering target tracking
target motion changing
model error
current statistical （CS）model
interacting multiple model （IMM）
