The research of detecting the outlier in data processing of relative navigation of tight formation flying

In order to meet with different mission requirements, autonomous control of tight formation flying requires high precision relative navigation. It's also worth mentioning that there is at least dm-level accuracy for semi-axis deviation in some distributed SAR satellite space system. But for hardware-in-the-loop simulation or orbital running mission of formation flying, there always are measurement outliers, such as uncertainty random noises and outliers, time hopping, data loss, which could detrimentally effect on convergence and stability of relative navigation algorithm. Simple methods such as the Out-Of-Limit (OOL) check, the 3σ check, which only verify if a certain parameter crosses soft(hard) upper or lower thresholds, is not robust enough to detect the various types of outliers. And if using the complicate method requires so much calculation that it's hardly applied to detect outliers in real time on board. This paper is organized as follows: Section 1 generally introduces the background, motivations and contributions of this paper. We will compare different detection methods by processing measured relative navigation data in section 2, all data was gathered in hardware-in-the-loop emulation. Then we propose a strategy that combines second-order differential method and least square method to detect outliers of different distribution. Finally, we use the navigation data with various outliers to validate the proposed strategy in last section, which can speed convergence up and enhance the stability of filtering.