基于犹豫模糊集的特征辅助数据关联算法

Feature Aided Data Association Algorithm Basedon Hesitant Fuzzy Sets

摘要:

针对复杂电磁环境下,对非线性近距小角度交叉运动的多个目标进行跟踪时存在跟踪精度不高和实时性较差等局限性问题,结合模糊数学的思想,提出一种基于犹豫模糊集的特征辅助数据关联新方法。对有争议的公共量测所属性进行模糊分配,将多目标跟踪问题转化为单目标跟踪问题,最后利用扩展卡尔曼滤波算法进行跟踪。在多种杂波密度和量测误差下的仿真结果均表明:对比多种现有数据关联算法,该方法可提高关联准确度,从而得到更高的跟踪精度,且实时性较强,更符合工程实现需求。

Abstract:

in the complex electromagnetic environment, there are some limitations suchas low tracking accuracy and poor real-time performance when tracking multiple targets inshort-range nonlinear cross-motion at small-angles, A new method of feature auxiliary dataassociation based on hesitant fuzzy sets is proposed, The attributes of controversial publicmeasurements are fuzzy allocated. and the multi-target tracking problem is transformed intoa single target tracking problem, Finally, the extended Kalman filter algorithm is used fortracking, The simulation results show that compared with many existing data associationalgorithms, the proposed method can improve the accuracy of correlation and obtain highertracking accuracy under various clutter densities and measurement errors. Besides, it hasbetter real-time performance, which is more in line with the needs of Engineering practice.