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针对在频域内计算X射线脉冲星信号延时量存在滞后性,进而难以为航天器自主导航提供实时信息的问题,提出将脉冲星信号延时估计转化为时域内标量估计的方法。首先,通过人工神经智能网络获得脉冲星信号的标准轮廓函数作为状态方程;应用粒子滤波算法对脉冲星信号延时量进行实时估计;其次,为了避免标准粒子滤波器中的粒子退化现象,推导并证明了一种新型粒子滤波算法;最后,推导出粒子滤波算法的精度函数,为航天器的导航策略提供参考。以航天器在轨运行中可能遇到的3种情况为背景,验证了所提粒子滤波算法的正确性与有效性。
Aiming at the problem of delay in calculating the delay of X-ray pulsar signal in the frequency domain and making it difficult to provide real-time information for spacecraft autonomous navigation, a method of transforming the pulsar signal delay estimation into the time-domain scalar estimation is proposed. First of all, the standard contour function of pulsar signal is obtained as an equation of state through the artificial neural network. The particle filter algorithm is used to estimate the pulsar signal delay in real time. Secondly, in order to avoid the particle degeneration in the standard particle filter, A new type of particle filter algorithm is proved. Finally, the precision function of the particle filter algorithm is deduced to provide a reference for the navigation strategy of the spacecraft. Based on the three kinds of situations that the spacecraft may encounter during orbit operation, the correctness and validity of the proposed particle filtering algorithm are verified.