Marthe D’Hooghe, arcsec; Céline Dubron, arcsec
https://doi.org/10.64861/EGAV6285
Keywords: Dataflow Architecture, Modeling, Space Debris, Space Environment, Space Situational Awareness, Integrating Diverse Data, SSA Instrumentation Development
Abstract:
Detecting and characterizing space debris from on-orbit platforms remains a challenging task, particularly due to uncertainties in debris observability and limitations in end-to-end data processing. While optical sensors provide valuable measurements, predicting how much debris can be reliably detected in orbit is inherently difficult, as detectability depends on factors such as object size, reflectivity, illumination conditions, relative motion, and sensor operational parameters.
Beyond sensing limitations, the effectiveness of debris observation missions is strongly influenced by the robustness of the onboard and ground-segment software responsible for processing, analyzing, and transporting measurement data. This paper discusses the challenges associated with debris detectability, highlights the need for a robust Debris Measurement Analyzer, and suggests an automated, modular dataflow architecture with a well-defined internal structure to enhance its capabilities. This work focuses on simulation architecture, automation, and uncertainty propagation rather than introducing new debris environment models.
Extending the work on the Debris Measurement Analyzer (Bartlomiej Koszek, 2024), a well-structured and streamlined dataflow architecture is established to enable robust simulation of the quantity and diversity of detectable space debris. Simulating multiple scenarios with clear parameters provides more reliable results to assess the in-orbit SSA possibilities and determine the properties of the relevant debris. This provides a new way to assess the capabilities of our SSA instruments in specific circumstances prior to deployment.
The use of an automated integration combined with a modular system design enables users to branch out at well-defined points within the workflow. As a result, multiple Monte Carlo simulations and sensitivity analyses can be established efficiently, each targeting different variables or assumptions. All simulations are initialized from the same baseline conditions, ensuring statistical consistency across the results. This approach provides insight into how variations in key parameters affect SSA instrument performance. The approach is evaluated through multiple Monte Carlo and sensitivity studies, highlighting improvements in reproducibility, traceability, and numerical consistency.
Automation is equally essential for reducing inconsistencies introduced by human error, which are often difficult to trace and can significantly complicate the interpretation of results. A well-structured dataflow architecture that minimizes manual intervention is therefore critical, restricting user interaction to deliberate and well-considered decision points. This is also critical for preserving numerical accuracy and controlling error propagation. By defining parameters only once and propagating internal, high‑precision outputs throughout the software stack, the workflow avoids error‑prone manual data transfer and reliance on rounded or formatted external outputs. As a result, the workflow becomes more robust, transparent, and reproducible, enabling more precise and consistent estimates of debris detectability.
To accomplish this, GMAT and MASTER are essential tools. GMAT was developed by NASA and is primarily used for the orbit of the SSA instruments. The other major external tool is MASTER, which contains the models of space debris that are fundamental for a realistic simulation. Together with information specific to the optical instruments we use for debris detection, we can simulate the likelihood of the instruments detecting space debris of various sizes in different operational scenarios. For GMAT, its own API is a logical choice for automation. MASTER, on the other hand, has no directly integrated automation options such as a CLI, API, or other scriptable interface. To access the crucial models within this tool, the use of DRAMA and ESABASE2 allows indirect automation with MASTER without going against ESA policies.
By establishing a well-defined dataflow throughout the Debris Measurement Analyzer, we mitigate problems such as data loss and safeguard the reproducibility of the outcomes. In addition, an unambiguous dataflow architecture also leads to improved interpretability of debris information and thus a more definite verification of SSA developments. This allows future development of SSA-enabled products to be faster, more readily verifiable, and better aligned with mission objectives.
Date of Conference: September 15-18, 2026
Track: Space Debris