Sierra Space and NVIDIA Collaborate to Enhance Orbital Debris Tracking with AI Technology

Sierra Space and NVIDIA Collaborate to Enhance Orbital Debris Tracking with AI Technology

Sierra Space, a leading commercial space company that is Building a Platform in Space to Benefit Life on Earth, announced a major advancement in space domain awareness. Leveraging the power of physics-informed neural networks (PINNs) and a collaboration with NVIDIA, Sierra Space is now able to predict the future locations of orbital debris.

As the volume of orbital debris continues to grow, the need for precise and reliable prediction methods has never been more critical to ensure safety and operational integrity of our space assets. Traditional models often fall short in accounting for the complex dynamics of space environments. However, by integrating the principles of physics directly into neural network architectures, Sierra Space has developed a solution that not only enhances prediction accuracy but also significantly reduces computational overhead.

"At Sierra Space, we are committed to pioneering innovative solutions that ensure the safety and operational integrity of our space assets. By leveraging physics-informed neural networks and collaborating with NVIDIA, we are setting a new standard in space domain awareness,” said Sierra Space CEO Tom Vice. “This advancement not only enhances our ability to predict the future locations of orbital objects with unprecedented accuracy but also significantly improves the computational efficiency of our models. Together, we are making space safer for all."

Innovative Use of Physics-Informed Neural Networks

PINNs represent a revolutionary approach to modeling and predicting the behavior of orbital debris. By embedding physical laws into the neural network's structure, these models can simulate the intricate interactions and forces acting on debris in orbit. This allows for more accurate predictions of future locations and potential intersections with operational spacecraft.

Sierra Space PINNs are trained to understand and predict the trajectories of debris by incorporating data from various sources, including satellite observations and historical tracking information. This data-driven approach, combined with the inherent understanding of physical laws, enables real-time predictions and alerts, ensuring that operational spacecraft can take timely and effective evasive actions.

Collaborating with NVIDIA: Powering Advanced Computation

Sierra Space’s innovations in space domain awareness initiatives is enabled by a collaboration with NVIDIA. Utilizing NVIDIA AI and accelerated computing for both training and inference, Sierra Space models can achieve heightened computational efficiency and speed. NVIDIA accelerated computing is specifically designed to handle the intensive computational demands of deep learning models, making them an ideal choice for Sierra Space’s PINN-based solutions.

NVIDIA's GPUs enable the processing of vast amounts of data simultaneously, significantly reducing the time required for model training and inference. This capability is crucial for real-time applications where timely predictions can make the difference between a safe maneuver and a potential collision.

"At Sierra Space, we are constantly pushing the boundaries of what is possible in space. By leveraging the power of NVIDIA accelerated computing and deep learning, we are not only enhancing the accuracy of our predictions but also ensuring the safety and operational integrity of our space assets,” said Dr. Joe Kopacz, Sierra Space’s Vice President of Software Engineering & AI Strategy“Our collaboration with NVIDIA has been instrumental in achieving the computational efficiency required for real-time applications. This advancement marks a significant milestone in our mission to protect the space environment and ensure sustainable operations for the future."

Click here to learn more about Sierra Space's Generative AI Space Solutions

Click here to learn more about NVIDIA's Artificial Intelligence Solutions

Publisher: SatNow
Tags:-  SatelliteLaunchGround

GNSS Constellations - A list of all GNSS satellites by constellations

beidou

Satellite NameOrbit Date
BeiDou-3 G4Geostationary Orbit (GEO)17 May, 2023
BeiDou-3 G2Geostationary Orbit (GEO)09 Mar, 2020
Compass-IGSO7Inclined Geosynchronous Orbit (IGSO)09 Feb, 2020
BeiDou-3 M19Medium Earth Orbit (MEO)16 Dec, 2019
BeiDou-3 M20Medium Earth Orbit (MEO)16 Dec, 2019
BeiDou-3 M21Medium Earth Orbit (MEO)23 Nov, 2019
BeiDou-3 M22Medium Earth Orbit (MEO)23 Nov, 2019
BeiDou-3 I3Inclined Geosynchronous Orbit (IGSO)04 Nov, 2019
BeiDou-3 M23Medium Earth Orbit (MEO)22 Sep, 2019
BeiDou-3 M24Medium Earth Orbit (MEO)22 Sep, 2019

galileo

Satellite NameOrbit Date
GSAT0223MEO - Near-Circular05 Dec, 2021
GSAT0224MEO - Near-Circular05 Dec, 2021
GSAT0219MEO - Near-Circular25 Jul, 2018
GSAT0220MEO - Near-Circular25 Jul, 2018
GSAT0221MEO - Near-Circular25 Jul, 2018
GSAT0222MEO - Near-Circular25 Jul, 2018
GSAT0215MEO - Near-Circular12 Dec, 2017
GSAT0216MEO - Near-Circular12 Dec, 2017
GSAT0217MEO - Near-Circular12 Dec, 2017
GSAT0218MEO - Near-Circular12 Dec, 2017

glonass

Satellite NameOrbit Date
Kosmos 2569--07 Aug, 2023
Kosmos 2564--28 Nov, 2022
Kosmos 2559--10 Oct, 2022
Kosmos 2557--07 Jul, 2022
Kosmos 2547--25 Oct, 2020
Kosmos 2545--16 Mar, 2020
Kosmos 2544--11 Dec, 2019
Kosmos 2534--27 May, 2019
Kosmos 2529--03 Nov, 2018
Kosmos 2527--16 Jun, 2018

gps

Satellite NameOrbit Date
Navstar 82Medium Earth Orbit19 Jan, 2023
Navstar 81Medium Earth Orbit17 Jun, 2021
Navstar 78Medium Earth Orbit22 Aug, 2019
Navstar 77Medium Earth Orbit23 Dec, 2018
Navstar 76Medium Earth Orbit05 Feb, 2016
Navstar 75Medium Earth Orbit31 Oct, 2015
Navstar 74Medium Earth Orbit15 Jul, 2015
Navstar 73Medium Earth Orbit25 Mar, 2015
Navstar 72Medium Earth Orbit29 Oct, 2014
Navstar 71Medium Earth Orbit02 Aug, 2014

irnss

Satellite NameOrbit Date
NVS-01Geostationary Orbit (GEO)29 May, 2023
IRNSS-1IInclined Geosynchronous Orbit (IGSO)12 Apr, 2018
IRNSS-1HSub Geosynchronous Transfer Orbit (Sub-GTO)31 Aug, 2017
IRNSS-1GGeostationary Orbit (GEO)28 Apr, 2016
IRNSS-1FGeostationary Orbit (GEO)10 Mar, 2016
IRNSS-1EGeosynchronous Orbit (IGSO)20 Jan, 2016
IRNSS-1DInclined Geosynchronous Orbit (IGSO)28 Mar, 2015
IRNSS-1CGeostationary Orbit (GEO)16 Oct, 2014
IRNSS-1BInclined Geosynchronous Orbit (IGSO)04 Apr, 2014
IRNSS-1AInclined Geosynchronous Orbit (IGSO)01 Jul, 2013