模拟训练系统中CGF的号手操作技能建模

针对模拟训练系统对CGF真实性的要求,从号手的操作技能入手,建立可以反映真实训练水平的号手操作技能模型。在分析操作技能相关原理和规律的基础上,采用时间序列分析的方法对其进行预测。采用了指数平滑预测模型,详细分析了其原理,并针对其在实际应用中的缺陷进行了改进。最后,通过两个实际案例验证了模型的可用性和可信性。所建模型反映了号手的操作技能,为模拟训练系统中CGF的真实性研究提供新的思路和方法。     

Robust cubature Kalman filter method for the nonlinear alignment of SINS

Nonlinear initial alignment is a significant research topic for strapdown inertial navigation system(SINS).Cubature Kalman filter(CKF)is a popular tool for nonlinear initial alignment.Standard CKF assumes that the statics of the observation noise are pre-given before the filtering process.Therefore,any unpredicted outliers in observation noise will decrease the stability of the filter.In view of this problem,improved CKF method with robustness is proposed.Multiple fading factors are introduced to rescale the obser-vation noise covariance.Then the update stage of the filter can be autonomously tuned,and if there are outliers exist in the observations,the update should be less weighted.Under the Gaussian assumption of KF,the Mahalanobis distance of the innovation vector is supposed to be Chi-square distributed.Therefore a judging index based on Chi-square test is designed to detect the noise outliers,determining whether the fading tune are required.The proposed method is applied in the nonlinear alignment of SINS,and vehicle experiment proves the effective of the proposed method.