Statistical Methods for Calibrating Spacecraft Sensors with Limited Overlap

Calibrating spacecraft plasma instruments is challenging when direct overlap between sensors is sparse, irregular, or confined to short time windows. We present a statistical framework for calibrating ionospheric plasma parameters derived from the Electric Propulsion Electrostatic Analyzer Experiment (ÈPÈE) to reference measurements from the Floating Potential Measurement Unit (FPMU) under such conditions. ÈPÈE, deployed on the International Space Station from March 2023 through April 2024, provides ion energy spectra during the peak of Solar Cycle 25, capturing highly variable topside ionospheric conditions. Our approach models each ÈPÈE energy spectrum as a Gaussian profile whose amplitude, mean energy, and width evolve smoothly in time. These latent parameters are coupled across adjacent time steps using an empirically weighted smoothness prior, allowing robust estimation even in the presence of noise and irregular sampling. Derived plasma quantities—density, floating potential, and temperature—are then statistically mapped to FPMU observations using regression models that incorporate nonlinear interactions and logarithmic scaling where physically appropriate. This framework enables calibration despite limited temporal overlap, reduces sensitivity to noise-driven dropouts, and preserves physically consistent temporal evolution. The resulting calibrated products improve data continuity and support studies of ionospheric variability and space-weather impacts on spacecraft systems and communications.