TLE outlier detection based on expectation maximization algorithm

The most widely-used data for civilian Space Situational Awareness are the Two-Line Element sets (TLEs) provided by the USSTRATCOM. TLEs play critical roles in space monitoring and analysis activities, and are also used in many applications requiring position information of space objects. However, TLEs suffer from uneven data quality, and it is usually necessary to perform outlier or anomaly detections before utilizing these ephemerides. There are many such procedures available in the published literature, and thresholds are usually obtained through simulations or from experiences, which lacks theoretical soundness and thus is inflexible to use. A new filter based on the Expectation Maximization (EM) algorithm is proposed to ease some restrictions, in which outlier thresholds are determined based on the variances estimated in the polynomial regression and prediction, which makes the new filter theoretically sound and more flexible to use. The new filter can be applied to detect outliers in TLEs of objects in any orbital regions, under various space environments, and enduring different operations such as orbit maneuvers. Multiple experiments are presented to demonstrate the effectiveness of the proposed filter to detect outliers of different magnitudes in TLEs. Simulated outliers in the mean motion and eccentricity with the relative magnitude >= 5 x 10(-3) are almost 100% detected, 74% - 98% of outliers in the inclination with the relative magnitude = 0:01 are detected, and 37% - 68% of those with the relative magnitude 5 x 10(-3) detected. In addition, sequences can be more accurately identified, which help detect outliers in different sequences, making the proposed filter more applicable to complex situations. (C) 2021 COSPAR. Published by Elsevier B.V.