Recent advances in performance and cost over the past years have led to increased use of
small satellites in scientific missions. When applied to a GRACE-like mission this could allow for
an improved spatial and temporal resolution. Small satellite constellations have an inherent
system redundancy due to their large distribution of risk and lack of single point failure.
Additionally, an increased number of satellites reduces the unit cost for production and therefore
the cost of replacement and maintenance of such a constellation.
A major challenge when designing an n-pair constellation is the vast search space of variables
available. Due to the nature of the discontinuous search space, derivative-based optimization
techniques cannot be used to scan the search space efficiently. To address this difficulty a
multi-objective genetic algorithm is used.
In this work we investigate the viability and limitations of a genetic algorithm-based optimization
and its objective function in order to generate satellite constellations aimed at recovering daily
Earth system mass changes. The resulting constellations have an inherent improved
spatio-temporal performance which will reduce temporal aliasing errors and allow the
characterization of daily mass-change effects. The performance of the designed constellations
has then been validated using high-fidelity numerical simulations.