Space Domain Awareness (SDA) is critical in today's increasingly congested and contested space environment. While traditional visible and short-wave infrared (SWIR) imaging has their place, thermal imaging in the medium-wave infrared (MWIR) and long-wave infrared (LWIR) bands offers unique advantages, including 24/7 operability regardless of solar illumination. This blog post explores how high-fidelity simulation using MuSES can accurately predict the dynamic thermal signatures of satellites, enabling a better understanding of their operational state.
The Need for Thermal Modeling in Space Domain Awareness
Characterizing a satellite and understanding its operational state are crucial for SDA. Long-wave infrared measurements are particularly well-suited for this task, as radiance in this thermal waveband is primarily driven by temperature. Analyzing thermal data can reveal insights into internal power generation, payload activity, and material composition. LWIR sensing also extends surveillance capabilities beyond terminator periods (when the satellite is illuminated by the Sun against a dark sky) and can potentially aid in determining satellite pose. LWIR is essential for effective and comprehensive SDA characterization. This is where MuSES comes in.
Introducing MuSES: A High-Fidelity Thermal Simulation Tool
MuSES (Multi-Service Electro-Optic Signature) is a commercially available software package developed by ThermoAnalytics that employs a first-principles physics-based approach to solve energy balance equations, accounting for radiation, conduction, and convection. MuSES allows for the creation of detailed 3D models of satellites, incorporating internal components, material properties, and even complex thermal links. It simulates transient solar loading, thermal radiation exchange with space and Earth, and heat transfer within the satellite. Crucially, MuSES can couple thermal and electrical solvers to realistically model solar panel efficiency and battery charge/discharge cycling, providing a holistic view of the satellite's thermal behavior throughout its orbit.
From Thermal Predictions to Radiance Maps
MuSES incorporates spectral optical surface properties, BRDF-based ray tracing, and generates radiance maps from predicted 3D temperature distributions. These maps can simulate sensor outputs, including radiometric sensor images, radiance signal levels, and contrast metrics for ground and space-based imaging platforms. Combining this with background radiance and noise estimates allows for the prediction of signal-to-noise ratios, which is crucial for sensor design and evaluation.
Case Studies: LEO and GEO Scenarios
To demonstrate the capabilities of MuSES, simulations were performed for a communications satellite in both Low Earth Orbit (LEO) and Geostationary Orbit (GEO). The satellite model incorporated realistic details, including solar panels with temperature-dependent efficiency, a battery pack, electronic loads, and MLI blankets. Simulations considered different operational states ("ON" and "OFF") to assess their impact on thermal signatures.
LEO Scenario:
Based on a Starlink TLE, the LEO simulation revealed the dynamic interplay between solar illumination, eclipse periods, battery charge/discharge, and temperature fluctuations. MuSES predicted transient LWIR and MWIR signatures from the perspective of a chasing satellite, highlighting the differences between the "ON" and "OFF" states. The impact of solar glints on MWIR signatures was also analyzed, demonstrating the importance of considering sensor blur.
GEO Scenario:
Based on a GOES-17 TLE, the GEO simulation focused on the impact of eclipse periods on the satellite's thermal signature. MuSES predicted the temperature variations of key components and their influence on the radiance, comparing and contrasting the “ON” and “OFF” satellites. The simulation also highlighted the potential of multi-spectral sensing, combining LWIR with visible band data to overcome the limitations of solar reflection-based imaging during eclipses.
Distinguishing Operational States: A Practical Application
A critical application of these simulations is the ability to distinguish between different satellite operational states. By comparing predicted radiance levels with observed data, it becomes possible to infer changes in a satellite's activity. The simulations demonstrated that, even with realistic sky background radiance, the difference in signal levels between "ON" and "OFF" states should be observable with meter-class telescopes and reasonable integration times. This suggests that MuSES-based predictions can be a powerful tool for analyzing observed data and potentially determining changes in the resident space object (RSO) state.
Conclusion and Future Directions
This work showcases the power of MuSES for predicting satellites' dynamic thermal signatures. By coupling thermal and electrical solvers, MuSES provides a comprehensive and accurate picture of satellite thermal behavior, including the impact of solar panel efficiency, battery cycling, and internal heat sources. The ability to generate radiance maps and simulate sensor outputs enables evaluating different sensor designs and developing robust SDA strategies.
Future work will focus on incorporating more complex thermal management systems (including heat pipes and active cooling) and other internal heat sources. The goal is to create even more realistic and detailed satellite models, enabling more accurate predictions and ultimately contributing to enhanced space domain awareness.
About the Author
Dr. Corey Packard is a Principal Engineer and Director of Research & Product Management here at ThermoAnalytics. He has more than 20 years of experience in remote sensing and physics-based simulation, with his primary technical focus being EO/IR signature prediction. Other technical efforts of Dr. Packard involve field test validations, phenomenological investigations, software development support, and predicting the behavior of optical sensor systems in turbulent atmospheres and from space-based platforms.
