Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adap...Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adaptability to maneuver mode mutations,a trajectory prediction method is proposed that integrates online maneuver mode identification with dynamic modeling.Characteristic parameters are extracted from tracking data for parameterized modeling,enabling real-time identification of maneuver modes.In addition,a maneuver detection mechanism based on higher-order cumulants is introduced to detect lateral maneuver mutations and optimize the use of historical data.Simulation results show that the proposed method achieves accurate trajectory prediction during the glide phase and maintains high accuracy under maneuver mutations,significantly enhancing the prediction performance of both three-dimensional trajectories and ground tracks.展开更多
两反式光学系统广泛应用于空间遥感、探测制导等领域,其成像质量是光学系统的核心指标,不仅依赖光学器件的制造精度,而且很大程度上受装配精度的影响。在实际工程中,光学系统装配后的成像质量很容易受到界面条件、装配位姿偏差等多源不...两反式光学系统广泛应用于空间遥感、探测制导等领域,其成像质量是光学系统的核心指标,不仅依赖光学器件的制造精度,而且很大程度上受装配精度的影响。在实际工程中,光学系统装配后的成像质量很容易受到界面条件、装配位姿偏差等多源不确定性因素的影响,即使相同的装配工艺参数也可能导致成像质量出现偏差。为此,提出一种两反式光学系统装配与成像的联合仿真方法,以能量集中度作为成像质量定量评价指标,辨识光学系统装配过程中的不确定性参数并进行不确定性度量,根据参数特点选择合理的采样方法,通过联合仿真方法得到不同装配误差条件下的光学系统成像质量数据。建立基于Matern5/2核函数的高斯过程回归(Gaussian Process Regression, GPR)拧紧力矩指向性代理模型,以及结合贝叶斯优化和蒙特卡洛模拟(Bayesian Optimization-Monte Carlo Simulation, BO-MCS)的不确定性优化算法,基于构建的原始数据集,实现光学系统装配不确定性建模分析与装配工艺参数鲁棒性优化。研究结果表明:与其他代理模型相比,所建立的GPR代理模型具有最小的成像质量预测误差(平均预测误差仅有1.95%);优化后的光学系统成像质量平均提升6.13%,波动半径平均减少14.05%,有效提高了光学系统装配后的成像质量一致性。展开更多
基金supported by the National Natural Science Foundation of China(12302056)the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZC20233445)。
文摘Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adaptability to maneuver mode mutations,a trajectory prediction method is proposed that integrates online maneuver mode identification with dynamic modeling.Characteristic parameters are extracted from tracking data for parameterized modeling,enabling real-time identification of maneuver modes.In addition,a maneuver detection mechanism based on higher-order cumulants is introduced to detect lateral maneuver mutations and optimize the use of historical data.Simulation results show that the proposed method achieves accurate trajectory prediction during the glide phase and maintains high accuracy under maneuver mutations,significantly enhancing the prediction performance of both three-dimensional trajectories and ground tracks.
文摘两反式光学系统广泛应用于空间遥感、探测制导等领域,其成像质量是光学系统的核心指标,不仅依赖光学器件的制造精度,而且很大程度上受装配精度的影响。在实际工程中,光学系统装配后的成像质量很容易受到界面条件、装配位姿偏差等多源不确定性因素的影响,即使相同的装配工艺参数也可能导致成像质量出现偏差。为此,提出一种两反式光学系统装配与成像的联合仿真方法,以能量集中度作为成像质量定量评价指标,辨识光学系统装配过程中的不确定性参数并进行不确定性度量,根据参数特点选择合理的采样方法,通过联合仿真方法得到不同装配误差条件下的光学系统成像质量数据。建立基于Matern5/2核函数的高斯过程回归(Gaussian Process Regression, GPR)拧紧力矩指向性代理模型,以及结合贝叶斯优化和蒙特卡洛模拟(Bayesian Optimization-Monte Carlo Simulation, BO-MCS)的不确定性优化算法,基于构建的原始数据集,实现光学系统装配不确定性建模分析与装配工艺参数鲁棒性优化。研究结果表明:与其他代理模型相比,所建立的GPR代理模型具有最小的成像质量预测误差(平均预测误差仅有1.95%);优化后的光学系统成像质量平均提升6.13%,波动半径平均减少14.05%,有效提高了光学系统装配后的成像质量一致性。
