基于区实序列变换的航迹关联算法

Track Association Algorithm Basedon the Interval-Real Sequence Transformation

摘要:

针对时变系统条件下的航迹关联问题,提出一种基于区实序列变换的关联算法。首先,利用线性最优化的方法,将上报航迹的不确定性描述为区间灰色序列；再在区实序列变换的基础上,利用实数序列间的灰关联度加权融合描述不同雷达上报航迹的关联相似度,通过判决给出关联结论。仿真结果显示了算法的有效性以及良好的抗差性能。

Abstract:

Aiming at the problem of track association in the existence of time-variable systemic error, an algorithm was proposed based on IRST. Firstly, the track uncertainty, caused by time-variable bias in radar network, was expressed into two grey interval sequences by solving the linear optimization problem. Secondly, based on the starting form TRST, a weighted fusion of several degrees of grey incidence, which were calculated independently formed several real-sequence matrixes, forms the degree of similarity, which was directly used to judge whether two tracks reported by different radars were the same one. Simulation results showed that this algorithm was efficient, and had a good performance in dealing with the track association under circumstances of systemic error.