Deep operator and SSA collaboration for space sustainability

Abstract

The space data association (SDA) has been providing reliable flight safety products for approximately 30 spacecraft operators for 14 years now. The service provides conjunction warnings and operator points of contact for around 700 spacecraft occupying all orbital regimes. The SDA's Space Data Center (SDC), built by AGI and maintained and operated by COMSPOC Corporation, has a proven track record of providing high availability space traffic coordination (STC) products since becoming operational on 15 July 2010.Earlier this year, the SDA, and its chief technical consultant, COMSPOC Corporation, supported the U.S. Department of Commerce (DOC) Geosynchronous Earth Orbit (GEO) and Middle Earth Orbit (MEO) Pilot project by providing comprehensively fused orbit solutions, ten-day orbit ephemeris and covariance predictions, and smoothed reference ephemerides for 100 active spacecraft. Most of these spacecraft are operated by SDA members and participants, allowing the operators to collaboratively contribute their maneuver plans, GPS NavSol measurements, active ranging, and passive RF observations, and authoritative spacecraft dimensions to the DOC Pilot project. For its part, COMSPOC employed its Space Situational Awareness (SSA) Software Suite (SSS)to comprehensively fuse this diverse set of spacecraft operator observations with COMSPOC's own observations from its global network of optical sensors.The first phase of this collaborative data fusion required the establishment of accounts, data connectivity, file transfer methods, and sensor calibration. This required about one month of technical interchange, provision of operator sensor locations and specifics, and COMSPOC SSS operator calibration of those sensors for each spacecraft.But once the data flows, sensor calibrations, and maneuver readers were completed, the second phase drew upon a nearly continuous stream of fused observations and maneuvers to yield accurate and timely predictive ephemerides and covariance time histories. These data products are well-suited to the collision avoidance problem.