基于修正模糊理论和D-S证据决策的航迹关联算法  

Track Synthetic Algorithm based on Modified Fuzzy Theory and D-S Evidence Decision

作　　者：王志伟 胡玉兰[1] 胡树杰[1] 刘炜 Wang Zhiwei;Hu Yulan;Hu Shujie;Liu Wei(School of Information Science and Engineering,Shenyang Ligong University,Shenyang 110159,China;School of Mechanical Engineering,Shenyang Ligong University,Shenyang 110159,China;School of Electronic Science and Engineering,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]沈阳理工大学信息科学与工程学院,辽宁沈阳110159 [2]沈阳理工大学机械工程学院,辽宁沈阳110159 [3]国防科技大学电子科学学院,湖南长沙410073

出　　处：《系统仿真学报》2019年第10期2019-2029,共11页Journal of System Simulation

基　　金：国家自然科学基金(61373089,61672360)

摘　　要：针对模糊推理航迹关联时滤波发散、隶属度函数性能下降、关联门限值确定难、复杂情况下关联效果变差等问题。提出基于修正模糊理论和D-S证据决策的航迹关联算法。算法在衰减记忆扩展卡尔曼滤波的基础上,提出对数似然函数自适应修正隶属度函数不定向畸变的方法,提出带冲突分解和一致聚焦的D-S证据决策的航迹关联方法,仿真结果表明,在高密度航迹条件下,新算法与原算法相比,提高了隶属度函数性能,解决了相关门限值确定难的问题,新的航迹关联算法可使航迹平均关联决策正确率提高5.3%。Aiming at the problems existing in fuzzy reasoning track association, such as the filter divergence, the membership performance degradation, the difficulty in determining association threshold value, and the association effect deterioration under complex circumstances, a track association algorithm based on modified fuzzy theory and D-S Evidence Decision is proposed. Based on the attenuated memory extended Kalman filter(AMEKF), a logarithmic likelihood function adaptive correction method is proposed to correct the disorientation of membership function,and a D-S evidence decision-making tracking association algorithm with conflict resolution and consistent focusing is proposed. The simulation results show that comparing with the original algorithm, the new algorithm improves the membership performance and lowers the difficulty in determining the correlation threshold value. This new association algorithm improves the accuracy of track average Association decision by 5.3%.

关 键 词：模糊综合决策 对数似然函数 隶属度函数 关联门限 冲突分解 D-S证据决策 

分 类 号：TP202[自动化与计算机技术—检测技术与自动化装置]