摘要
提出了一种新颖的快速数据关联算法,减少了滤波中关联概率的计算量.该算法利用多个并行改进的最大熵模糊聚类对各个目标的有效观测进行聚类,采用聚类得到的模糊隶属度来重建滤波中的联合关联概率,并在联合关联概率中引入了比例因子避免航迹的合并;此外,分析了算法中差异因子的特性,考虑了杂波密度对它的影响,使得能够有效剔除无效观测,进一步减少计算量.仿真实验结果表明,提出的方法是一种有效的快速数据关联算法,跟踪性能要优于现有的数据关联算法.
A novel fast data association method is proposed, which reduces the computational load of the association probability in filtering. Firstly, the candidate measurements of each target are clustered with the aid of the modified maximum entropy fuzzy clustering. Then the joint association probability is reconstructed by utilizing the fuzzy membership degree of the target and measurement. At the same time, in order to avoid the track coalescence, the scaling factor is introduced to modify the joint association probability. Moreover, the characteristic of the discrimination factor is analyzed and the influence of the clutter density on it is also considered, which enables the algorithm to eliminate those invalidate measurements and reduce the computational load. Finally, simulation results show that the fast data association algorithm is effective, and that the performance of tracking is better than that of the existing ones.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2006年第2期251-256,共6页
Journal of Xidian University
基金
国家部委预研资助项目(41101050108)
关键词
最大熵模糊聚类
数据关联
联合关联概率
maximum entropy fuzzy clustering
data association
joint association probability
