现代防御技术 ›› 2025, Vol. 53 ›› Issue (6): 82-90.DOI: 10.3969/j.issn.1009-086x.2025.06.009

• 导航、制导与控制 • 上一篇    下一篇

考虑等式约束交互多模型瞄准误差估计方法

刘阳, 王仁杰, 杨桂清   

  1. 北京机电工程总体设计部,北京 100854
  • 收稿日期:2024-07-25 修回日期:2024-11-04 出版日期:2025-12-28 发布日期:2025-12-31
  • 作者简介:刘阳(2000-),男,黑龙江哈尔滨人。硕士生,研究方向为飞行器导航与制导。

Aiming Error Estimation Method Using Interactive Multiple Model with Equality Constraint

Yang LIU, Renjie WANG, Guiqing YANG   

  1. Beijing System Design Institute of Mechanical-Electrical Engineering,Beijing 100854,China
  • Received:2024-07-25 Revised:2024-11-04 Online:2025-12-28 Published:2025-12-31

摘要:

针对雷达天线罩瞄准误差实际值相对理论值偏差散布大,难以通过地面数据精确补偿的问题,提出了一种多模型滤波算法。考虑瞄准误差随导引头体视线角分布差异,分析地面低速运动目标特性,采用交互多模型方法结合量测增广的无迹卡尔曼滤波算法提高瞄准误差估计精度。末制导阶段导引头体视线角由大至小转变,该方法先处理大视角量测数据,判别目标运动状态;再处理小视角量测数据,实时估计瞄准误差。仿真结果表明,所提算法是考虑等式约束情况下估计瞄准误差的有效手段,在估计精度方面与传统方法相比有较大提高。

关键词: 天线罩瞄准误差, 交互多模型, 无迹卡尔曼滤波, 等式约束, 状态估计

Abstract:

To address the significant deviations between actual and theoretical aiming error values of the radar radome, which are difficult to compensate for precisely using ground data, this paper proposed a multi-model filtering algorithm. Considering the distribution differences of aiming error due to line-of-sight angle dispersion of the seeker body, the paper analyzed the characteristics of low-speed ground motion targets and employed an interactive multi-model approach combined with the measurement-augmented unscented Kalman filter algorithm to improve the accuracy of aiming error estimation. During the terminal guidance phase, the line-of-sight angle of the seeker body gradually decreased. This method processed large-angle data to identify target motion and then small-angle data to estimate aiming error in real time. Simulation results indicate that the proposed algorithm is an effective approach for estimating aiming error under equality constraints, significantly improving estimation accuracy compared to traditional methods.

Key words: radome aiming error, interactive multi-model, unscented Kalman filter(UKF), equality constraint, state estimation

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