Today’s satellites are inherently inflexible and purpose-built for a single mission. In contrast, the community is exploring disruptive approaches towards in-orbit reconfiguration and on-board data processing. One of the most promising approaches is to use COTS components and open source frameworks, which decrease time-to-market and could even increase the pool of developers/users on the satellite.

We aim to develop a novel control theory methodology using input-output process models for error mitigation in signal processing data-paths that operate in radiation prone environments.  In this approach, difference equations are used to characterize the process with perturbation corresponding to faulty digital circuits.

This activity developed high-performance data compression software with embedded data analysis features for radio-frequency, multi-band images and video. It offers excellent compression ratios and quality levels with very small computing requirements, which even allow for real-time operation on modest processors. Tests on data from ESA's OPS-SAT satellite reveal image ratios over 1:10 with excellent visual quality, and radio-frequency ratios over 1:3 with nearly identical spectrograms thanks to a smart lossy approach.

Many use cases for insight generated in orbit require the integration and calibration of raw data from multiple instruments and vantage-points – a future vision sometimes referred to as ‘hybrid observation’. Such a capability would be a foundational requirement in the processing chain for rapid follow-up observations especially when we need to estimate quantitative variables (e.g. soil moisture or water pollutants).

‘STARCOP’ is an initiative to use machine learning and multiple satellites with diverse detection capabilities to quickly detect methane leaks and send notifications in near real-time. In this project Trillium Technologies developed machine learning models to automatically detect methane in hyperspectral and multispectral imagery. Their hyperspectral model (HyperSTARCOP) is able to capture more than 90% of plumes in test data while reducing the false positive rate by 39% when compared to state-of-the-art models.

Future spacecraft missions require high accuracy (in relative and absolute kinematics as well as attitude parameters).

Solar panel suppliers are developing a partially automated solar generator assembly process. A GSTP G617-241TA de-risk Activity: "Identification and baseline validation of solar cell test and measurement techniques for highly automated space solar generator manufacturing" was in the process to be completed, where measurement techniques have been identified and initially verified to be suitable to control the quality of the solar generator during automatic manufacturing.

The inertial navigation market has a need for accelerometers with high sensitivity, low noise, low cost and low power consumption. ESA has identified high accuracy accelerometer units as a priority in a 2019 roadmap. The current state of art products are not able to deliver. Here we introduce a novel accelerometer design, using concepts originally developed for gravitational wave detector physics, which will enable an accelerometer to meet these requirements.

Time-to-Digital Converters (TDCs) are crucial elements in many sensor readout systems, such as time-of-flight sensors, laser ranging devices, LiDAR, ultrasonic sensors, particle detectors, etc. To the best of our knowledge, there is currently no rad-hard TDC component available on the market, which could meet the targeted specification defined in this project. The goal of this project is prototyping and lab testing of a radiation-hardened single-chip picosecond TDC in a commercial 65 nm CMOS technology.