US-12627372-B2 - Advanced mission control predictive systems for low earth orbit semi-autonomous satellites

Abstract

The disclosed system provides advanced mission control predictive systems for low earth orbit semi-autonomous satellites. In operation, the system may include a satellite transit behavior prediction module configured to train a neural network based on current and past satellite orbital transit paths so as to predict future transit paths. Knowing precise future satellite transit paths enables a ground-based satellite control system to more efficiently (and with greater accuracy) control certain operations of the satellites. For example, a ground-based satellite control system can prepare for communications to commence at a particular time. Legacy systems can only predict one or two days in advance However, using a neural network that not only takes into consideration historical transit data, but also learned details such as solar wind, cloud patterns, etc., the neural network predicts future satellite transit paths with statistically-certain accuracy leading to a much narrower cone of uncertainty.

Inventors

• Niraj PRASAD
• Deeptanshu Prasad

Assignees

• QUANTUM GENERATIVE MATERIALS LLC

Dates

Publication Date

20260512

Application Date

20240918

Claims (20)

1. 1 . An advanced mission control system, comprising: a ground-based satellite control system comprising computers with processors and memory; a communication module comprising radio frequency communication equipment including transceivers and antennas for communicating between the ground-based satellite control system and one or more satellites; a satellite transit behavior prediction module comprising machine learning software configured to execute on a prediction processor configured to: receive current and past orbital transit paths of the one or more satellites, train a neural network based on the current and past orbital transit paths, and predict future orbital transit paths for the one or more satellites using the trained neural network; and a satellite management module comprising mission control software configured to execute on a satellite management processor for managing the one or more satellites based on the future orbital transit paths.
2. 2 . The advanced mission control system of claim 1 , wherein the ground-based satellite control system includes a user interface for managing operations of the one or more satellites.
3. 3 . The advanced mission control system of claim 2 , wherein the user interface includes a display for visualizing the current and future orbital transit paths of the one or more satellites.
4. 4 . The advanced mission control system of claim 1 , wherein the satellite transit behavior prediction module uses a machine learning model to predict the future orbital transit paths.
5. 5 . The advanced mission control system of claim 4 , wherein the machine learning model is trained using historical satellite transit data.
6. 6 . The advanced mission control system of claim 5 , wherein the historical satellite transit data includes data related to orbital inclination, right ascension of an ascending node, eccentricity, argument of perigee, mean anomaly, and mean motion.
7. 7 . The advanced mission control system of claim 1 , wherein the satellite management module includes a scheduling module for scheduling satellite operations based on the future orbital transit paths.
8. 8 . The advanced mission control system of claim 1 , wherein the communication module uses radio frequency communication to communicate between the ground-based satellite control system and the one or more satellites.
9. 9 . The advanced mission control system of claim 1 , wherein the ground-based satellite control system includes a data processing module for preprocessing satellite data.
10. 10 . The advanced mission control system of claim 9 , wherein the data processing module includes a data cleaning module for removing or correcting erroneous or missing data.
11. 11 . The advanced mission control system of claim 9 , wherein the data processing module includes a data transformation module for converting the satellite data into a suitable format or scale.
12. 12 . The advanced mission control system of claim 9 , wherein the data processing module includes a data normalization module for adjusting values of the satellite data to a common scale.
13. 13 . The advanced mission control system of claim 1 , wherein the ground-based satellite control system includes a model training module for training machine learning models.
14. 14 . The advanced mission control system of claim 13 , wherein the model training module uses one of: a supervised learning model, an unsupervised learning model, a semi-supervised learning model, or a reinforcement learning model to train the machine learning models.
15. 15 . The advanced mission control system of claim 1 , wherein the communication module is configured to optimize a timing and sequencing of data to or from the one or more satellites by segmenting the data into micro-batch packets of data.
16. 16 . The advanced mission control system of claim 15 , wherein the segmenting is based on the future orbital transit paths.
17. 17 . The advanced mission control system of claim 15 , wherein the segmenting is based on one or more areas of interest or a location of the ground-based satellite control system.
18. 18 . The advanced mission control system of claim 1 , wherein the ground-based satellite control system includes a data visualization module for visualizing satellite data.
19. 19 . The advanced mission control system of claim 1 , wherein the ground-based satellite control system includes a data analysis module for analyzing satellite data.
20. 20 . The advanced mission control system of claim 1 , wherein the ground-based satellite control system includes a data mining module for mining satellite data.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This Patent Application claims the benefit of and priority to U.S. Provisional Patent Application No. 63/571,281, entitled “DISTRIBUTED SATELLITE CONSTELLATION MANAGEMENT AND CONTROL SYSTEM,” filed Mar. 28, 2024; and U.S. Provisional Patent Application No. 63/574,141, entitled “DISTRIBUTED SATELLITE CONSTELLATION MANAGEMENT AND CONTROL SYSTEM,” filed Apr. 3, 2024, all of which are assigned to the assignee hereof; the disclosures of all prior Applications are considered part of and are incorporated by reference in this Patent Application. This Patent Application is a continuation-in-part of and claims the benefit of and priority to U.S. patent application Ser. No. 18/600,486, entitled “DEEP GENERATIVE MODELS FOR INVERSE DESIGN OF INORGANIC ATOMISTIC STRUCTURES,” filed Mar. 8, 2024; and U.S. patent application Ser. No. 18/397,993, entitled “GENERATIVE ATOMISTIC DESIGN OF MATERIALS,” filed Dec. 27, 2023, all of which are assigned to the assignee hereof; the disclosures of all prior Applications are considered part of and are incorporated by reference in this Patent Application. FIELD OF THE INVENTION The present disclosure generally relates to satellites, and more specifically, satellite constellation management and control. BACKGROUND Satellite constellations may include groups of satellites working together in a coordinated manner to provide global or near-global coverage for various applications such as communication, earth observation, and navigation. These constellations can range from a few satellites to hundreds or even thousands of satellites, depending on the specific application and coverage requirements. Managing and controlling these satellite constellations can be a complex task due to the large number of satellites involved, the distance from a control point, and the dynamic and harsh space environment in which they operate. Such ability to control satellites typically may involve various aspects such as mission planning, satellite scheduling, communication management, and fault detection and recovery. However, current systems to control satellites are often confronting issues of delay (in delivering updates to satellite nodes), outdated infrastructure, lack of communication (such as when a satellite node exits a window of transmission), etc. As such, there is thus a need for addressing these and/or other issues associated with the prior art. SUMMARY This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. In some aspects, the techniques described herein relate to a satellite optimization management system, including: a natural language processing module configured to receive and interpret user input expressed in natural language to determine a user's intent and map it to specific tasks for a satellite, wherein the natural language processing module uses a neural network to determine the user's intent; an execution module configured to generate and execute satellite command sequences based on operational tasks derived from the user's intent; and a communication interface configured to facilitate data transmission between the satellite and ground control. In some aspects, the techniques described herein relate to a satellite management system, including: a plurality one or more satellites in a constellation; a ground-based control system for managing operations of the one or more of satellites, the ground-based control system including satellite control and payload delivery systems, wherein the ground-based control system manages interaction between the one or more of satellites and at least one of: at least one ground station, at least one mission control station, or at least one data center; a messaging module configured to route messages between the one or more of satellites and the ground-based control system; and a micro-batch delivery module configured to transmit and receive the messages to and from the one or more of satellites. In some aspects, the techniques described herein relate to an advanced mission control system, including: a ground-based satellite control system; a communication module for communicating between the ground-based satellite control system and one or more satellites; a satellite transit behavior prediction module configured to: receive current and past orbital transit paths of the one or more satellites, train a neural network based on the current and past orbital transit paths, and predict future orbital transit paths for the one or more satellites using the trained neural network; and a satellite management module for managing the one or more satellites based on the future orbital transit paths. In some aspects, the techniques described her