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BAE Systems to Launch its First Multi-sensor Satellite Cluster

September 14, 2022 by Jake Hertz

A new satellite cluster will do machine learning (ML) on board, and be able to send intelligence securely back to Earth.

Compared to consumer and commercial systems, government and military sectors tend to be followers in many areas of electronics. But they still drive certain areas that involve reliability and security. Because of this, electrical engineers need to continually push the state of the art to meet the stringent demands.

Last week, BAE Systems announced plans for a next-generation satellite cluster in 2024 that will serve government and military purposes. In this article, we’ll take a look at traditional computing challenges with military satellites and how BAE Systems’ new cluster is addressing the issues.

 

Military Satellite Challenges

One of the most powerful tools for government and military purposes in recent history has been satellite surveillance.

Modern surveillance satellites are often equipped with a number of high-performance sensors including cameras for satellite imagery and RF detectors. Along with this, more recent development has been the advancement of machine learning (ML) to the point where most satellites now rely on some level of ML for applications like image analysis and detection.

 

Satellites tend to rely on terrestrial data centers for computing.

Satellites tend to rely on terrestrial data centers for computing. Image used courtesy of Ilchenko et al 

 

A challenge emerges here, however, as many of the currently deployed satellites are not equipped with the computational power and storage resources necessary to facilitate the ML computation on board. Instead, satellites tend to rely on cloud computing, where collected data is communicated to a terrestrial data center where storage and processing can take place.

This method is undesirable for a number of reasons, but arguably the most significant reason is security. When government entities are using satellite surveillance, the resulting information is often extremely sensitive and meant to be kept confidential.

The cloud computing scheme is not congruent with this. That’s because offloading the data to and from a data center on earth opens up a number of potential security risks.

 

BAE Systems’ Azalea Cluster

Last week, BAE Systems announced that it is set to launch its first multi-satellite cluster into low earth orbit in 2024. According to the company, expertise for the satellite development and launch was added by its acquisition of In-Space Missions last year.

The satellite cluster, called Azalea, is going to consist of a group of interconnected satellites that will leverage a variety of sensors to help observe, analyze, and monitor terrestrial occurrences. Meant for military and government customers, the Azalea cluster will provide information collected from visual, radar, and RF sensors to help governments understand threats and hazards from space.

Additionally, as a result of a partnership with Finnish ICEYE, the Azalea cluster will be capable of communicating synthetic aperture radar (SAR), which is a means of remote earth imaging that is useful in creating 2D and 3D images of the earth. 

 

BAE’s Azalea Cluster.

BAE’s Azalea Cluster. Image used courtesy of BAE Systems. (Click on image to enlarge)

 

One of Azalea’s major value propositions is the fact that it’s equipped with the computing infrastructure necessary to perform data analyses on board. While the specifications of the computing platform are unknown, BAE tells us that data will be analyzed by onboard edge processors to ensure higher security for customers. In this way, the Azalea system can keep sensitive data safely onboard the satellite while minimizing the need for terrestrial 

Furthermore, BAE tells us that the cluster will be designed to be fully reconfigured while in orbit through over-the-air (OTA) software updates. The benefit here is enabling flexibility and versatility for the cluster to allow optimizations for future missions and customers.