刘振亚, 高敏, 许路铁. 基于理想弹道的全捷联弹药视线角估计方法[J]. 红外与激光工程, 2018, 47(4): 417008-0417008(7). DOI: 10.3788/IRLA201847.0417008
引用本文: 刘振亚, 高敏, 许路铁. 基于理想弹道的全捷联弹药视线角估计方法[J]. 红外与激光工程, 2018, 47(4): 417008-0417008(7). DOI: 10.3788/IRLA201847.0417008
Liu Zhenya, Gao Min, Xu Lutie. Line-of-sight angle estimation method of strapdown munition based on ideal trajectory[J]. Infrared and Laser Engineering, 2018, 47(4): 417008-0417008(7). DOI: 10.3788/IRLA201847.0417008
Citation: Liu Zhenya, Gao Min, Xu Lutie. Line-of-sight angle estimation method of strapdown munition based on ideal trajectory[J]. Infrared and Laser Engineering, 2018, 47(4): 417008-0417008(7). DOI: 10.3788/IRLA201847.0417008

基于理想弹道的全捷联弹药视线角估计方法

Line-of-sight angle estimation method of strapdown munition based on ideal trajectory

  • 摘要: 针对惯性测量元件不能满足低成本制导弹药作战需求问题,提出一种基于理想弹道的全捷联激光半主动末制导弹药视线角估计方法。该方法根据弹目相对运动模型及导引头量测模型建立非线性滤波系统;针对弹体运动参数在末制导段变化范围较小的特点,通过分析弹体运动参数对系统不确定性的影响,将理想弹道弹体运动参数标准值作为滤波系统参数;利用激光半主动导引头量测信息,结合容积卡尔曼滤波对弹目视线角进行估计。数字仿真实验结果表明:在小扰动条件下,弹目视线倾角与偏角末制导段的均方根误差分别为0.182与1.668,其最大估计误差分别为0.259与2.913,具有较好的估计精度与鲁棒性能。

     

    Abstract: Aiming at the problem that inertial measurement unit can not meet the operation requirement of the low cost guidance munition, a line-of-sight (LOS) angle estimation method of strapdown semi-active laser-guided munition based on ideal trajectory was proposed. According to the relative motion model between ammunition and target and the seeker measurement model, the nonlinear filter system was built. Aiming at the features that the ballistic parameters varied in the small range, this algorithm took the ideal trajectory parameters as filter parameters by analyzing the impact from projectile motion parameters on the system uncertainty; the LOS angel was estimated by using the measurement information from laser semiactive seeker and the cubature Kalman filter (CKF). The digital simulation results showed that under the condition of small disturbance, the root mean square errors (RMSE) of LOS inclination and declination are 0.182nd 1.668, the max estimation errors of them are 0.259 and 2.913. The algorithm has better estimation precision and robust property.

     

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