多特征信息融合的中心群跟踪算法

Centroid Group Tracking Algorithm Fusing Multi-characteristic Information of Targets

传统的中心群跟踪(CGT)算法通过跟踪群的几何中心来实现对群整体运动的估计,但当存在杂波时,群目标的空间分布会受到杂波干扰,使群的中心位置受到影响,导致跟踪误差增大。文中基于多特征信息融合的思想,利用传感器获得的电磁辐射特征信息,将运动状态信息与时、频域特征信息进行融合,通过比较相关波门内的量测值与预测值之间的关联度,以达到滤除杂波的效果,完成对群中心的状态估计。仿真结果表明,文中算法在均方根误差和平均有效量测点数等方面相比传统算法有所改善,证明了算法的有效性。

The traditional centroid group tracking(CGT)algorithm estimates group collective movement by tracking the centroid of group.However,the space distribution of group targets and the centroid of group will be influenced by clutter,result in increasing tracking errors.The improving method proposed in this paper makes good use of characteristics information of electromagnetic radiation acquired by sensors and fuses the information with the motion status,time domain,and frequency domain characteristics information of target.By calculating the degree of association between the qualified measurement and the prediction measurement,the clutter is eliminated and the status of centroid is estimated.Numerical simulations show that the new algorithm outperforms the traditional algorithm on the root mean square errors and average effective measurement quantity,resulting in the improvement in tracking performance.