AI for space traffic management: Q&A

Space is becoming increasingly congested. The challenge of managing an ever-growing amount of space debris increases with time. Scientific models estimate staggering numbers, revealing over 29,000 objects larger than 10 cm, 670,000 objects larger than 1 cm, and more than 170 million objects larger than 1 mm, all orbiting Earth.

The space debris poses a significant threat to operational spacecraft, with potential catastrophic consequences. A collision with a 10-cm object could result in the fragmentation of a typical satellite, a 1-cm object could disable a spacecraft and breach the International Space Station's shields, and a 1-mm object has the potential to destroy critical subsystems on board a spacecraft.

By leveraging AI capabilities, we can enhance our ability to monitor, predict, and mitigate the risks associated with space debris, ultimately ensuring the safety and sustainability of activities in Earth's orbit. Here's an insightful Q&A session with Chiara Manfletti, CEO of Neuraspace, where she shares her thoughts on the use of Artificial Intelligence (AI) in Space Traffic Management (STM). 

How do you use AI for Space Traffic Management?

Neuraspace uses AI and machine learning to predict and prevent collisions in space and to optimise the use of resources for improved space operations. We ingest, analyse, and process complex data and, with the help of machine learning algorithms, offer calculated, updated, and accurate suggestions to satellite operators.

What is the advantage of using AI as compared to the existing systems?

Cognitive technologies can support safety and security needs in space in multiple ways:

(1) Automation of processes;

(2) Gaining insight through data science;

(3) Engaging with users.

Process automation is perhaps the most common type. Cognitive insight, the second most common, uses algorithms to detect patterns in vast volumes of data and interpret their meaning.

It’s a data-driven world. With that, we know that we need to adapt systems that can process and analyse data. That said, AI is the future and will be the way businesses will be inclined to operate.

In space traffic management (STM), where data and information from various sources needs to be computed and analysed, data fusion, AI and ML are implemented to process data, which otherwise would take loads of personnel time, as well as to recognise patterns which would otherwise be difficult to identify.

For businesses, as a result, there are more than 75k euros in savings per year per satellite. Additionally, manpower costs are considerably reduced because of an STM platform such as Neuraspace. It saves time and effort in analysis and computation and offers a reliable solution in the form of manoeuvring suggestions that are based on accurate computation and analysis. 

Current solutions rely on manual processes, traditional technologies and sensors, and cannot cope with the 15x increase in space assets. In contrast, our STM platform, with improved data-mining using data fusion and proprietary ML algorithms, offers significant benefits for businesses. These include a 100x increase in decision-making efficiency, an 80% reduction in staff time for safety-critical operations through automation, and 20% more planning time. They also offer optimised spacecraft/constellation management for extended lifespans and increased commercial revenue. The enhanced data analysis also reduces false alerts by 30% and eliminates unnecessary manoeuvres, resulting in substantial cost savings and reduced human intervention.

How does your technology work and how do you train it?

Neuraspace is a software-as-a-service (SaaS) solution that addresses the critical challenges of space traffic and debris management. It's a powerful tool for space domain awareness and automated decision-making in the ever-evolving space environment. It is built on five core components:

Data fusion: Neuraspace collects and integrates data from various sources, such as space debris information, collision detection messages, ephemeris files, and more. This comprehensive data fusion enhances space domain awareness and aids in mission planning while reducing uncertainties related to conjunction events.

Artificial intelligence (AI) and machine learning (ML): Neuraspace leverages AI and ML to analyse complex data and identify patterns. This enables more accurate collision predictions and risk mitigation.

Automation for resource optimisation: The platform offers a 24/7 AI-enabled tool that automates decision-making processes, providing valuable insights and suggestions based on operator constraints and priorities.

Orbital dynamics: Neuraspace uses its knowledge of the space environment to optimise mission operations and risk management. This includes precise orbit determination, conjunction analysis, manoeuvre optimization, and predictive analytics.

Data and dedicated tracking services: Neuraspace provides dedicated analysis and resolution services for high-criticality conjunctions, which can also be requested by customers separately. 

How is your technology currently implemented?

Our product fuses large quantities of data from different sources into a data warehouse including space debris images, CDMs, ephemeris files, state vectors from established catalogues and satellite owners, space weather nowcasts and forecasts, and information about the assets being managed. The data is then analysed using AI and ML, providing our customers with automated AI-enabled collision risk prediction along with manoeuvre suggestions to support operators through risk mitigation. 

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