Abstract
Space debris fragments too small to track can still reach speeds capable of disabling a spacecraft. The number of such fragments remains uncertain, making it difficult to assess risks to satellites supporting Earth-critical services such as broadband and GPS. We combine radar detection with a Bayesian model to adaptively choose where a radar should point next, aiming to reduce uncertainty. We analyzed about 24 hours of data from each of the two European Incoherent Scatter Scientific Association (EISCAT) radars: the Tromsø ultra high frequency radar and the EISCAT Svalbard radar. We first used a Generalized Matched Filter to search for radar signals consistent with moving objects. Tests with 4,000 simulated object signals in raw voltage data led us to select cell-averaging Constant False Alarm Rate (CA-CFAR) detection. For external validation on another radar, we applied the CA-CFAR detector selected using the Tromsø recordings to the Svalbard recordings without retuning. The method found 155 events at Tromsø and 222 at Svalbard. Of these, 106 and 119, respectively, could not be confidently matched to cataloged objects. We used the Tromsø rate of unresolved events to calibrate a Bayesian population model informed by the Orbital Debris Engineering Model (ORDEM). With this model, we compared radar pointing schedules across 300 simulated worlds, giving each schedule 24 observations. Adaptive pointing with service weights reduced uncertainty at the final observation by 41.2%, 15.4%, and 5.35% relative to fixed, random, and preplanned schedules, respectively. The additional improvement over unweighted adaptive pointing was estimated at 1.55% (95% interval: -0.47% to 3.53%), a small difference that was not statistically significant. The simulations provide strong evidence that adaptive pointing can reduce uncertainty around hidden debris using the same number of observations, potentially improving the information used to assess risks to important satellite services.