MDA has been awarded contributions from the Canadian Space Agency (CSA) involving next generation Earth observation technologies. The smartEarth initiative has funded the two following projects:

smarthEarth Projects

• Deep Learning for Classification of SAR-Derived Forest Change

The objective of this project is to develop new artificial intelligence algorithms that will efficiently process large amounts of space-based radar data to detect and map forest changes, which in turn will facilitate decisions on remedial actions to ensure the continued abundance of forest lands in Canada and around the world.

• Detecting Object Behaviour of Interest Using Deep Learning

This innovative system will leverage the latest advances in Deep Learning to detect objects of interest in satellite images and seamlessly fuses them using a statistically rigorous framework, forming the basis for a new analytics engine within next-generation ground systems that can manage large volumes of data across multiple sensor platforms.

Today’s contributions are in addition to funding provided to MDA in the last few months under the CSA’s Space Technology Development Program (STDP), including five research and development projects and two feasibility studies. These contributions, in the areas of robotics, vision systems and sensors, and satellite communications technologies, include:

STDP Research and Development Projects

• Robotics Servicing System Arm-joint Development

This funding will advance the development of the arm joint on the robotic manipulator for MDA’s commercial line of space robotics which is intended for use in commercial space applications such as on-orbit inspection, assembly and servicing of space assets. Leveraging MDA’s 40-year heritage and operational experience, this funding will support MDA in being the first to market for a commercial robotic arm at a commercial price point while maintaining our position as a global leader in space robotics.

• Onboard Closed-loop Tracking for Space Situational Awareness Applications

MDA is developing an onboard computing solution for detecting and tracking Resident Space Objects using Machine Learning (ML) techniques. The ML object classification algorithm developed in the investigation will enable future autonomous spacecraft tasked with monitoring space debris and satellites to perform real-time, on-orbit threat assessment.

• Advanced Antennas and Payload Technologies for Software Defined Satellites

The objective is to develop a set of antenna and payload technologies that will provide generic solutions that can be configured (software-defined) for the specific needs of each mission, including repurposing the satellite during its life. This development is crucial because in addition to delivering the flexible solutions that are demanded by the operators, the reconfigurability enables economies of scale through hardware standardization, ultimately helping our customers be successful by making satellites more affordable.

• Beam Hopping Technology Development for HTS Communication Systems

This contract is for the development of the end-to-end system architecture to implement beam hopping over satellite, as well as the hardware development of key enabling sub-systems within the payload. This will allow MDA to offer greater payload flexibility to satellite operators by accommodating irregular and time variant traffic requests across the satellite coverage region. This will help our customers reduce market uncertainty surrounding 15-year investment decisions, and represents a very efficient flexible alternative to fully digital satellites at a fraction of the cost and with much higher power efficiency.

• Hemispherical Camera for Space Situational Awareness

The number of satellites and debris in Low Earth Orbit (LEO) is increasing rapidly and few options exist to remove or mitigate their presence. Limitations of existing technologies prevent efficient and comprehensive space situational awareness. MDA will develop and characterize a compact hemispherical lens assembly coupled to an innovative and high-sensitivity detector. The performance of the hemispherical camera will push range and resolution beyond what is available elsewhere.

STDP Feasibility Studies

• AI-Enabled Non-Cooperative Sensor Mission Validation Tool

This project seeks to accelerate time to market for satellite rendezvous and proximity operations tools. Current tools for designing and testing tracking systems require detailed evaluations to be performed by trained developers over several weeks or months. MDA will investigate applications of machine intelligence to accelerate the evaluation and training systems for the configuration of model based tracking systems (also known as non-cooperative tracking).

• Large-Scale True Time Delay Optical Beamforming for RF Phased Array

This project aims to evaluate the feasibility of implementing true time delay (TTD) beamforming using photonic integrated circuits for a large-scale phased array such as those used in low cost, large Low Earth Orbit (LEO) mega-constellations. This innovative technology has the potential of improving significantly the performances of those phased arrays in addition to reducing their size, weight, power consumption and mass. MDA undertakes this feasibility study with an academic partner that has extensive expertise in the field and can support us in this phase of the development as well as subsequent phases as applicable.

“These funding awards are exciting news for our team at MDA, and will help us advance the development of leading-edge technologies in areas where we see opportunities to grow our business, including space robotics and sensors, geointelligence and digital communications. We value our decades-long partnership with the CSA and its continued commitment to help Canadian companies grow and prosper in the global space sector.” – Mike Greenley, Chief Executive Officer, MDA