Synthetic Database of Space Objects Encounter Events Subject to Epistemic Uncertainty
Description
Six datasets are presented. Database_System1.csv and Database_System2.csv include samples of space object close encounters subject to epistemic uncertainty on the relative position. Plausibility_System1.txt and Belief_System1.txt include the values of the Cumulative Plausibility and Belief Curves (CPC and CBC, respectively) of each sample included in Database_System1.csv. Plausibility_System2.txt and Belief_System2.txt contain the value of the CPC and CBC of each sample included in Database_System2.csv. All of them are synthetic databases created using computer simulation to obtain the results presented in: "Sanchez and Vasile, On the Use of Machine Learning and Evidence Theory to Improve Collision Risk Management, Acta Astronautica, Special Issue for ICSSA2020, In Press". Database_System1.csv database is constituted by 9,000 samples and 45 columns and a header, while Database_System2.csv is formed by 28,800 samples and 45 columns and a header. These databases come from a set of, respectively, 5 and 14 different families of encounter geometries defined by the range of values that can be assigned to the bounds of the intervals for the uncertain variables, assumed to be provided by two sources of information. The uncertain variables are considered to be affected by epistemic uncertainty. These uncertain variables are the upper and lower bounds of the interval for the components of the miss distance, [µx, µy], on the impact plane (B plane), the standard deviation of the relative position of the objects projected on the B plane, [σx, σy], and the Hard Body Radius of the combined objects, HBR. The dataset is completed with Collision Risk Management related parameters, like miss distance and covariance matrix of the uncertain ellipse projected on the B plane enclosing all samples defined by the intervals of the uncertain variables, the Probability of Collision (Pc) of this ellipse or the elapsed time to the Time of Closest Approach (TCA), with Evidence Theory related parameters, like Belief (Bel) and Plausibility (Pl) of certain values of Pc, and the class of the event according to the classification detailed in [1]. Plausibility_System1.txt and Belief_System1.txt are constituted by 34 columns and 9,000 rows containing the Pl and Bel for Pc values and the corresponding Probabilities of Collision necessary to build the CPC and CBC of the events in Database_System1.csv, while Plausibility_System2.txt and Belief_System2.txt are constituted by 34 columns and 28,800 rows containing the Pl and Bel for Pc values and the corresponding Probabilities of Collision values necessary to build the CPC and CBC of the events in Database_System2.csv. These databases have a potential usage by the Machine Learning community interested in Space Traffic Management as well as for the space community, as space operators interested on introduce epistemic uncertainty on collision risk assessment. They contribute to building a scarce field like the databases of encounter events.
Files
Steps to reproduce
Refers to: "Sanchez and Vasile, On the Use of Machine Learning and Evidence Theory to Improve Collision Risk Management, Acta Astronautica, Special Issue for ICSSA2020, In Press". and "Sanchez and Vasile, Synthetic Database of Space Objects Encounter Events Subject to Epistemic Uncertainty. Data in Brief, In Press".