摘要
传统近程主动防护系统目标跟踪算法不考虑车辆运动因素,但车辆实际运动参数对目标跟踪精度影响很大,进而导致系统整体防护性能降低,因此,需开展动基座下雷达目标跟踪算法研究。在动基座下,实现了雷达量测空间配准,进而基于UKF算法,引入雷达径向速度量测信息,采用比例对称采样策略,提出了一种改进的UKF算法。通过Monte-Carlo仿真,验证了该算法能够显著提高目标跟踪精度与收敛性,满足近程主动防护系统实时高精度跟踪要求。
The target tracking algorithm of the traditional short-range active protection system does not take into account the vehicle motion factors, but the actual vehicle motion parameters have a great influence on the target tracking accuracy, which leads to a decrease in the overall protection performance of the system. Therefore, the radar target tracking algorithm on the moving base must be studied. Under the moving base, the radar measurement space registration was realized. Then based on the UKF algorithm, an improved UKF algorithm was proposed by introducing radar radial velocity measurement information and adopting scale symmetric sampling strategy. Through Monte-Carlo simulation, it is verified that the algorithm can significantly improve the target tracking accuracy and convergence, and meet the realtime high-precision tracking requirements of the short-range active protection system.
作者
孙金雯
陈曦
杜忠华
陆谦
许国杰
SUN Jinwen;CHEN Xi;DU Zhonghua;LU Qian;XU Guojie(School of Mechanical Engineering,Nanjing University of Science and Technology, Nanjing 210094,China)
出处
《弹箭与制导学报》
北大核心
2019年第1期5-10,共6页
Journal of Projectiles,Rockets,Missiles and Guidance
基金
中央高校基础科研业务费专项资金(30915012201)资助
关键词
近程主动防护
目标跟踪
动基座
UKF
比例对称采样
short-range active protection
target tracking
moving base
unscented kalman filtering (UKF)
scale symmetry sampling
