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Ruag Space has teamed up with Swedish developer Stream Analyze to port AI analytics software to a space-qualified processor board for large scale deployment on constellations of small satellites.

Swarms of hundreds or thousands of small satellites are increasingly used for bringing data and internet services to Earth. To position, communicate and dispose such large amounts of satellites, AI is increasingly important. Adding AI decision making on the satellite allows results of requests to be sent over the radio links, rather than raw data.

Stream Analyze sa.engine will be ported to the Ruag Lynx board being designed and built at RUAG Space’s site in Gothenburg, Sweden. The 6U SpaceVPX board uses a 3000MIPS quad core ARM processor with FPGA co-processor that is 250 times more powerful than the On Board Computers that RUAG currently delivers to ESA programs.

“We have been very early with this development. We are seeing that Artificial Intelligence and Machine Learning is starting to arrive in space development programs and now we have a computer ready that perfectly matches the requirements of these customers”, said Senior Vice President Satellites at RUAG Space.

Analyzing the network behaviour, such as traffic patterns or other characteristics in a software defined satellite dynamic communication network, allows for optimizing data routes through the network and hence the performance of the complete communication system.

The sa.engine allows the network optimization to be performed in real-time onboard the satellite and enables the operator of the satellite will be able to interact directly with the satellite’s sensors and query any kind of questions. The sa.engine itself requires only a few megabytes and is hardware and software independent, so it can be integrated into the complete standard portfolio of RUAG Space’s on-board computers and into almost any other satellite computer. As sa.engine is scalable, it will be able to support any fleet of satellites and to interact with and learn from other satellites.

“This cooperation makes satellites ready for intensified use in future of Artificial Intelligence,” said Linder. Moving the intelligence from servers on ground into edge processing on the satellite in space has several advantages.

“It is possible to optimize response times and utilization of the data downlink resource which is often a bottleneck. Especially as sensors are getting more powerful and producing more and more data in the satellites which would currently need to be sent to Earth for processing,” he said.

“For us at Stream Analyze to add value and new capabilities to others through edge analytics is what we are all about. An example of such a new capability will be for others to analyze the data provided by the satellite sensors on the fly, as it is produced and without latency, allowing for faster response times and decisions,” said Nils Sahlberg, Vice President and Head of Strategy and Business Development at Stream Analyze. Decision support can be downlinked to ground much quicker than with a complete data set. It is also possible to make the decisions autonomously directly on the satellite. Data can be analyzed on board the satellite to make decisions in real-time by combining different sensor inputs. Monitoring data related to the satellite itself will also enable a more optimized satellite operation, performance and lifetime.

The current development of analytics algorithms is both time consuming and has limited capability to be changed after launch. Having a programmable engine in space allows models ot be updated. “With the sa.engine at hand, one doesn’t need to finalize the algorithms and the satellite capabilities before launch. You can literally develop and deploy as you go – changing the model development process and the satellite operations fundamentally – generating a better, more adaptable, and cheaper operation,” said Jan Nilsson, CEO at Stream Analyze.

streamanalyze.com; www.ruag.com/space

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