水面目标在应对来袭反舰武器时通常会做出机动规避行为，在主动观测模式下，针对长探测周期间隙中获取机动目标信息缺失导致较大的解算误差，进而影响我方反舰武器的命中概率的问题，本文提出基于信息融合的Singer模型机动目标跟踪算法，将被动探测信息与主动探测信息相融合并构建虚拟量测值，通过信息的共享和互补，提升了目标量测信息的准确性。同时，为了避免边界条件导致算法发散，在滤波中引入渐消因子，提升了算法的鲁棒性。本文针对2种典型目标机动策略模型进行仿真验证，并进行对比分析，结果表明，在2种目标机动策略模型下，本算法相比于主动观测模式的解算结果在平均解算精度上分别提升了39.85%和45.71%，验证了本算法的有效性。
Surface targets typically perform evasive maneuvers in response to incoming anti-ship missiles. In active observation modes, the long detection intervals can lead to significant errors in calculating the position of maneuvering targets, which in turn affects the hit probability of our anti-ship weapons. To address this issue, this paper proposes a maneuvering target tracking algorithm based on the Singer model and information fusion. This algorithm integrates passive and active detection information and constructs virtual measurements. By sharing and complementing information, the accuracy of target measurement information is enhanced. Meanwhile, in order to avoid the boundary conditions causing the algorithm to diverge, the asymptotic cancellation factor is introduced into the filtering, which improves the robustness of the algorithm. This paper conducts simulation validations and comparative analyses on two typical target maneuver strategies. The results show that under these two maneuver strategy models, the proposed algorithm improves the average calculation accuracy by 35.51% and 40.84% respectively compared to the active observation mode, demonstrating the effectiveness of the algorithm.
2024,46(24): 172-177 收稿日期：2024-5-9
DOI：10.3404/j.issn.1672-7649.2024.24.030
分类号：TN953
基金项目：西北工业大学“双一流”学科建设基金项目（24GH0201265）
作者简介：田雨(2000-),男,硕士,研究方向为鱼雷总体技术
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