利用零相位Kaiser窗滤波器改善MEX多普勒数据定轨精度

精密轨道确定在深空探测中至关重要，而定轨数据中的白噪声会影响定轨性能。基于零相位分析，比较了FRR（forward-filter reverse-filter reverse-output）、RRF（reverse-filter reverse-filter forward-output）和Matlab中的filtfilt这3种滤波器的优劣，设计了一种零相位Kaiser窗低通滤波器。利用火星快车号（Mars Express，MEX）的仿真数据和实测数据验证了零相位Kaiser窗低通滤波器的性能，结果发现滤除白噪声后MEX数据的定轨精度有了显著改善。双程测速数据残差均方根（root mean square，RMS）减小为原来的1/3左右，达到了0.031 mm/s；轨道位置和速度与欧空局（European Space Agency，ESA）精密轨道的差异明显变小。该滤波算法作为定轨前的数据预处理可以提高定轨精度，从而为中国火星探测器的轨道数据处理提供一定的参考。

Improving MEX Orbit Determination Accuracy by Using Zero-Phase Kaiser Window Filter

Precise orbit determination is crucial in deep space exploration, and white noise in orbital tracking data can affect orbit determination performance. Based on the analysis of zero phase, we compared three kinds of filters, FRR, RRF and filtfilt in Matlab, and designed a zero-phase low pass filter using Kaiser window. The performance of the filter was verified by simulated and measured tracking data of MEX. After filtering white noise in the MEX measurement, the accuracy of MEX orbit determination could be significantly improved. For the two-way Doppler tracking data, the RMS of the velocity residuals was reduced to about one third of the original, that is, in the level of 0.031 mm/s; the difference of orbital position and velocity with the ESA reconstructed orbit was significantly reduced. The filtering process can be used as data preprocessing to improve orbit determination accuracy, and can also provide some reference for Chinese Mars exploration mission.