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AI enabled Intelligent Satellite Networks

Satellite communication can provide many benefits and complement terrestrial networks. They can provide service continuity, to provide network access over uncovered and under-covered areas. They can provide backup or service ubiquity, to ameliorate the network availability in cases of temporary outage or destruction of a ground network due to disasters. They can also provide service scalability, to offload traffic from the ground networks.


Although satellite communication offers improved global coverage and increased communication quality, it has several challenges. These include resource management, network control, network security, spectrum management, and energy usage of satellite networks are more challenging than of terrestrial networks.


Satellite resources are expensive and thus require efficient systems involving optimizing
and time-sharing.


The high mobility of the space segments, and the inherent heterogeneity between the satellite layers (GEO, MEO, LEO), the aerial layers (unmanned aerial vehicles (UAVs), balloons, airships), and the ground layer make network control, network security, and spectrum management challenging.


Meanwhile, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has been steadily growing as a research field and has shown successful results in diverse applications, including wireless communication.


One area in which the applications of AI are being thoroughly investigated is in satellite operations, in particular to support the operation of large satellite constellations, which includes relative positioning, communication, end-of-life management and so on.


In addition, it is becoming more common to find ML systems analysing the huge amount of data that comes from each space mission. The data from some Mars rovers is being transmitted using AI, and these rovers have even been taught how to navigate by themselves.


A recent study focused on the management of complex constellations for which novel automated procedures are being studied to reduce the active workload of ground operators. Automation of both the ground and space segments will reduce the need for human intervention –  especially for large constellations, automated collision avoidance manoeuvres could be a real help.


Other studies carried out under ESA’s Basic Activities include investigating how a swarm of picosatellites can evolve a collective consciousness, and looking into how artificial intelligence can be used in advanced mission operations and technologies, as well as in innovative security concepts, mechanisms and architectures.


Satellites orbiting Earth also require more autonomy, as they need to make more frequent collision avoidance manoeuvres to evade increasing amounts of space debris.


Multibeam satellite systems make it possible to reduce the size of earth stations and hence the cost of the earth segment. Frequency re-use from one beam to another permits an increase in capacity without increasing the bandwidth allocated to the system. A satellite payload using multibeam coverage must be in a position to interconnect all network earth stations and consequently must provide interconnection of coverage areas.


Lockheed Martin is Using AI to Enhance Cybersecurity

Lockheed Martin is applying Artificial Intelligence across the product life cycle – from production to satellite operations. AI is increasing the speed at which satellites can be developed and tested. During key testing milestones like Thermal Vacuum (TVAC), we use an in-house AI system called T-TAURI, which combs through testing data to analyze anomalous results in a fraction of time – significantly decreasing schedule.


AI is also enhancing space capabilities through pathfinder nanosat missions like Lockheed Martin-developed Pony Express and La Jument. Both missions are testing SmartSat, a software-defined satellite platform which uses containerized apps that can be easily uploaded in-orbit. By training an algorithm on the ground, we can upload it to a SmartSat-enabled satellite and run it in real time. One app being tested will be SuperRes, an algorithm that can automatically enhance the quality of an image and enable exploitation and detection of imagery produced by lower-cost, lower-quality image sensors. SmartSat is also opening the door to heuristic pattern and anomaly detection, enabled by AI, which improves cybersecurity resilience on-board with automatic updates as new threats emerge.


Lockheed Martin is also working on ways to autonomously command constellations of satellites of all sizes. As increasing numbers launch, satellites will need to autonomously make trajectory changes like slew maneuvers, a process that is both time and processor-intense for operators. Compass ML is moving those calculations to the edge so vehicles can plan their next maneuvers with or without assistance from the ground or respond to tips and threats.

— Linda Foster, director of Innovation at Lockheed Martin Space Mission Solutions


Raytheon Intelligence & Space Uses AI to Perform Space-Based Battle Management

Traditionally, data processing and exploitation occurs through ground systems when satellites are overhead to download the data. That takes time, which we may not have, particularly with constellations that have hundreds of satellites’ data to process. At Raytheon Intelligence & Space, we’re working on advanced on-board processing using space-qualified signal processors capable of hosting powerful AI and ML applications, where the satellite becomes the data collector, exploiter and disseminator – the brain and the nervous system. That will enable satellites to deliver actionable intelligence directly to the right person at the right time.


We’ve also developed advanced software AI and ML algorithms to perform mission-specific space-based battle management, command, control, and communications applications. When you’re on the front lines, in any domain, time is of the essence. Our AI and ML algorithms enable machine-speed processing for high volumes of data generated from proliferated LEO constellations of sensors.

— Jason Kim, business development executive of Space & C2 Systems at Raytheon Intelligence & Space



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