EXPECTED ACCURACY OF DENSITY RECOVERY USING SATELLITE SWARM GRAVITY MEASUREMENTS

Asteroids are of particular interest in the modem space industry due to their potential for advancing knowledge about the origins of the solar system. Identifying and characterizing asteroids with sustainable resources for future space exploration is critical. Additionally, limited knowledge of near-Earth asteroids' physical characteristics such as shape, density, gravity field, and composition pose a challenge to any manned exploration. A stochastic gradient method from the field of deep learning is applied to the problem of asteroid density recovery, with implications for target selection and mining. The algorithm outperforms the predicted observability, and is minimally affected by noisy measurements.