### Improving the Coverage of Earth Targets by Maneuvering Satellite Constellations

Abstract

Satellite constellations around earth can be used for
observing and/or communicating with targets on the
surface. this work addresses maneuvering existing
satellite constellations in order to improve coverage of
multiple targets over a timespan of 30 to 120 days.
a direct relationship is established between a satellite's
orbital geometry and the coverage provided by that
satellite. this is accomplished by (1) identifying the view of
the satellite orbit from an inertial sphere centered on the
earth, and (2) utilizing information from all the orbital views
across the target's inertial latitude in order to arrive at
lower and upper bounds on coverage.
altering a satellite orbit also alters the coverage that it
provides. gauss' variational equations are used to find
maneuvering strategies that effect maximal changes in
orbital geometry. these distinct maneuvering strategies
are then compiled into a list that will be used in the
subsequent optimization.
the problem of reconfiguring existing satellite
constellations in order to improve coverage is phrased as a
multiobjective optimization problem. in it, each satellite in
the satellite constellation can be assigned any one of the
maneuvering strategies as well as an allotment of
propellant that will be consumed during the maneuvering.
these become the parameters in the optimization problem.
an algorithm that is well suited for solving this optimization
problem is a multiobjective evolutionary/genetic algorithm.
such an algorithm is capable of handling, without further
transformations, the three difficulties with the stated
problem: (1) continuous and discrete optimization
parameters (e.g. propellant allotment, a distinct
maneuvering strategy, etc.), (2) nonlinear optimization
objectives (e.g. total coverage time, number of coverage
windows, etc.), and (3) multiple optimization objectives (e.g.
total coverage time over target 1, total coverage over
target 2, etc.). this algorithm is implemented by adopting
features from other similar algorithms.
finally, a set of examples is investigated in order to study
the effectiveness of this approach.