Method, system, and medium for processing satellite orbital information using a generative adversarial network

A system for processing satellite orbital information using a generative adversarial network (GAN), said system comprising:

• an electronic device including a processor and memory;
• wherein the electronic device is configured to:
• (a) generate a machine learning discriminator model that takes in a pair of orbital position observations, and returns a boolean indicating whether or not said pair represents a real orbit;
• (b) generate a second machine learning generator model that takes in an orbital position observation, a vector encoding a desired timestep, and a randomly generated salt vector, and returns a corresponding propagated orbital position observation at the desired timestep;
• (c) train the discriminator model utilizing, as the pair of orbital position observations input thereto, a combination of real orbital position observations and propagated orbital position observations from the generator model;
• and
• (d) train the generator model using as a loss input such propagated orbital position observations that the discriminator model determines do not represent a real orbit, and backpropagate accordingly;
• then at least one of:
• (i) identify, using the trained discriminator model, a pair of orbital position observations that do not represent a real orbit;
• and
• (ii) generate, using the trained generator model, and based upon a real orbital position observation, a counterfeit propagated orbital position observation that the discriminator determines to represent a real orbit.

A non-transitory computer-readable storage medium embodying a computer program, the computer program comprising computer readable program code that when executed by one or more electronic processors causes the processor(s) to:

• (a) generate a machine learning discriminator model that takes in a pair of orbital position observations, and returns a boolean indicating whether or not said pair represents a real orbit;
• (b) generate a second machine learning generator model that takes in an orbital position observation, a vector encoding a desired timestep, and a randomly generated salt vector, and returns a corresponding propagated orbital position observation at the desired timestep;
• (c) train the discriminator model utilizing, as the pair of orbital position observations input thereto, a combination of real orbital position observations and propagated orbital position observations from the generator model;
• and
• (d) train the generator model using as a loss input such propagated orbital position observations that the discriminator model determines do not represent a real orbit, and backpropagating accordingly;
• then at least one of:
• (i) identify, using the trained discriminator model, a pair of orbital position observations that do not represent a real orbit;
• and
• (ii) generate, using the trained generator model, and based upon a real orbital position observation, a counterfeit propagated orbital position observation that the discriminator determines to represent a real orbit.

A computer-implemented method for processing satellite orbital information using a generative adversarial network (GAN) for orbital maneuver detection and deceptive maneuver generation, said method comprising:

• (a) generating a machine learning discriminator model that takes in a pair of orbital position observations, and returns a boolean indicating whether or not a detected orbital maneuver has occurred;
• (b) generating a second machine learning generator model that takes in an orbital position observation, a vector encoding a desired timestep, a randomly generated salt vector, and a second vector representing a simulated maneuver, and returns a propagated orbital position observation at the desired timestep as a result of the simulated maneuver;
• (c) training the discriminator model utilizing, as the pair of orbital position observations input thereto, a combination of real orbital position observations and propagated orbital position observations from the generator model;
• and
• (d) training the generator model using as a loss input generated propagated orbital position observations that the discriminator model determines do not represent a maneuver, and backpropagate accordingly;
• then at least one of:
• (i) detecting, using the trained discriminator model, and based upon a pair of real orbital position observations, whether an orbital maneuver has been performed;
• and
• (ii) generating, using the trained generator model, a deceptive orbital maneuver that is below an edge of detection of the discriminator.