利用增量相位和星光仰角增强XNAV

Enhancement of XNAV Utilizing Incremental Phase and Star Elevation

针对X射线脉冲星导航系统(XNAV)中过程噪声统计特性难以准确获取,对其不当假设导致滤波器估计性能不佳的问题,提出基于自适应差分卡尔曼滤波器(ADDF)的多信息融合算法。为了降低导航误差,在传统脉冲星计时观测的基础上,增加恒星星光仰角及两个时刻间的相位增量观测量,共同增强XNAV。首先,分别建立计时观测模型、相位增量模型及星光仰角模型;然后将多信息测量模型集成到卫星轨道动力学方程中,以建立ADDF滤波模型;最后对所提方法进行仿真验证。实验结果表明,在相同的初始状态和初始噪声误差条件下,与传统X射线脉冲星导航算法相比,多信息融合算法能将导航位置估计精度提高70%以上,位置估计误差降低到200 m左右,速度估计精度提高40%以上,且ADDF性能优于无迹卡尔曼滤波器。

The X-ray pulsar navigation system(XNAV)is a nonlinear system.It is difficult to obtain the statistical characteristics of the process noise accurately.The improper assumption of XNAV leads to poor performance of the filter estimation.A multi-information fusion navigation method based on the adaptive divided difference filter(ADDF)is proposed.In order to reduce the navigation error,based on the traditional pulsar timing observation,the star elevation angle and the incremental phase between the two moments are appended to enhance the XNAV together.First of all,the time observation,incremental phase and star elevation model are established respectively.Then,this measurement model is integrated into the spacecraft orbit dynamics to build the ADDF filter model.Finally,the proposed method is verified by simulation.Under the condition of the same initial state and initial noise error,the experimental results show that the multiinformation fusion algorithm can reduce the estimated value of navigation position error to about 200 m.Compared with the conventional X-ray pulsar navigation algorithm,the accuracy of position estimation and velocity estimation are increased by more than 70%and 40%respectively.Moreover,The performance of ADDF is better than that of the unscented Kalman filter.