Satellite Placement

Suppose you have a set of N satellites and k targets on Earth that you want to observe. Each of your satellites has varying capabilities for Earth observation; in particular, the amount of ground that they can observe for a set amount of time is different. Since there are k targets, you would like to have k constellations to monitor said targets. How do you group your satellites into k constellations such that the average coverage of each constellation is maximized? This is the question that we will be addressing in this demo!

Note: in this demo we are assuming that N is a multiple of k.

Usage

To run the demo,

python satellite.py

It will print out a set of satellite constellations.

Code Overview

The idea is to consider all possible combinations of satellites, eliminate constellations with particularly low coverage, and encourage the following type of solutions:

• Constellations that have better coverage
• Satellites to only join one constellation
• A specific number of constellations in our final solution (i.e. encourage the solution to have k constellations)

Code Specifics

• The score_threshold - used to determine bad constellations - was assigned an arbitrarily picked number
• In the code, we add weights to each constellation such that we are favoring constellations with a high average coverage (aka high score). This is done with bqm.add_variable(frozenset(constellation), -score). Observe that we are using frozenset(constellation) as the variable rather than simply constellation as
  1. We need our variable to be a set (i.e. the order of the satellites in a constellation should not matter, {a, b, c} == {c, a, b}). In addition, add_variable(..) needs its variables to be immutable, hence, we are using frozenset rather than simply set.
  2. Since are there are more ways to form the set {a, b, c} than the set {a, a, a} -> {a}, the set {'a', 'b', 'c'} will accumulate a more negative score and thus be more likely to get selected. This is desired as we do not want duplicate items within our constellation. (Note: by "more ways to form the set", I am referring to how (b, c, a) and (a, c, b) are tuples that would map to the same set, where as (a, a, a) would be the only 3-tuple that would map to the set {a}.)

References

G. Bass, C. Tomlin, V. Kumar, P. Rihaczek, J. Dulny III. Heterogeneous Quantum Computing for Satellite Constellation Optimization: Solving the Weighted K-Clique Problem. 2018 Quantum Sci. Technol. 3 024010. https://arxiv.org/abs/1709.05381

License

Released under the Apache License 2.0. See LICENSE file.