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Identifying NaK Objects in Space Debris

Identifying NaK Objects in Space Debris. Johanne Christensen. Problem. Soviet RORSAT satellites were deployed between 1967 to 1988 Some of the satellites had nuclear reactors

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Identifying NaK Objects in Space Debris

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  1. Identifying NaK Objectsin Space Debris Johanne Christensen

  2. Problem • Soviet RORSAT satellites were deployed between 1967 to 1988 • Some of the satellites had nuclear reactors • The nuclear satellites were decommissioned in the 1980s by boosting the satellite to a graveyard orbit and jettisoning the reactor • NaK coolant was leaked due to a faulty seal in 16 satellites • Can we identify which groups of NaK objects originated from each satellite? RORSAT

  3. Data • 1076 data objects with 10 attributes • Data comes from the MIT Haystack Radar in MA • 75° Elevation East is examined • Haystack can only identify objects larger than 5mm • Data contains location information (alt, range, range-rate, inclination) and wavelength information • Wavelength information gives the information on the composition of the object • Additional data about the RORSAT satellites (orbit heights and inclination) was also given

  4. Preprocessing • Correlated all the columns • Alt and Range have very high correlation >.99 • High correlation among the wavelength data (PP, OP, RCS) • Low correlation between the location data and wavelength data • Can omit the wavelength data • Tells us that the objects are all NaK

  5. Clustering Inclination and Altitude by Cluster • Need to remove outliers, since they will skew the clusters • Used DBScan and removed noise point • Used visualization and removed a point with significantly higher rdot • K-means with 16 clusters • Attributes Used: Alt, Inclination, Range-Rate

  6. Refining Clusters • Running the clustering algorithm produces several clusters with centroids under 800km • Does not match satellite data • Many of the objects are below 700km, while 15 of the satellites are orbiting at 900km • Orbital decay is likely – most of the low altitude objects are small (<1cm)

  7. Refining Clusters • Removed objects at low altitude, below 700km • Clustered with K-means, 30 clusters Inc and Alt Before and After Removing Low Altitudes

  8. Postprocessing • Merged similar clusters based on inclination and range-rate • Accounts for orbital decay, since objects decay at different rates

  9. Merged Clusters

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