Corey Packard, ThermoAnalytics, Inc.; Casey Demars, The Tech7 Company; Logan Canull, ThermoAnalytics, Inc.; David Tyler, The Tech7 Company; Chris Rodgers, The Tech7 Company; Zachary Edel, ThermoAnalytics, Inc.; Timofey Golubev, ThermoAnalytics, Inc.
Keywords: EOIR, simulation, MuSES, signature, contract, EO/IR, LWIR, modeling, SDA, SSA, 3D, thermal, daylight, characterization
Abstract:
Space-domain awareness (SDA) via remote thermal imaging has benefits over conventional visible and SWIR imaging, potentially providing insight into the operational state of observed satellites. LWIR sensors provide capability for daytime and nighttime imaging, as demonstrated with the 3.6 meter telescope at the AMOS site on Maui, Hawaii. Satellite-to-satellite imaging using an LWIR sensor has been demonstrated in GEO with Mission Extension Vehicle-2 (MEV-2) capturing imagery of Intelsat 901 on approach for docking. In order to understand the capability that thermal dominant bands such as MWIR and LWIR play in SDA, a robust simulation capability must be developed to evaluate these phenomena.
The computational complexity required for radiative transfer simulation has previously resulted in a lag in the progress of satellite-focused thermal modeling in comparison to similar tools developed for visible and SWIR sensors. In this work, we demonstrate the ability of MuSES to predict both internal and external temperature distributions for 3D satellite models. MuSES uses orbital boundary conditions to simulate transient solar loading, thermal radiation to space and from Earth, as well as conductive and radiative heat transfer from internal components such as electronics. Additionally, solar panel efficiency and battery cell charge/discharge cycling can be realistically incorporated via the coupled thermal/electrical multi-physics solvers in MuSES. Surface facets are attributed with spectral optical surface properties to generate radiance maps via BRDF-based ray tracing of the 3D temperature distributions. This allows radiometric signal levels and contrast metrics to be generated using sensor simulations for both ground- and space-based imaging platforms. This requires combining the predicted band-integrated spectral radiance of the satellite with the appropriate background radiance (i.e., sky radiance for Earth-based observations) and estimates of shot noise fluctuations based on integration time.
The output of this process is a useful indication of the signal-to-noise ratio expected for the corresponding sensor model and provides a means to compare sensor design parameters. Using these tools, we demonstrate how solar panel efficiency and internal heat sources can impact surface temperature distributions and infrared signal levels during observations of satellites in LEO and GEO. Comparisons are made between Earth-based and space-based (satellite-to-satellite) observations. Additionally, the significance of solar panel efficiency (which varies with module temperature and solar incidence angle, and is zero for inoperable satellites) on radiometric signature is explored.
Date of Conference: September 17-20, 2024
Track: Satellite Characterization