Unconditional and conditional signal modeling
I am Filippo Torrisi, a PhD student at ISAE-SUPAERO, where I conduct research in statistical signal processing for GNSS-R as part of the GLITTER project, funded by the European Union. My work lies at the intersection of signal processing, estimation theory, and remote sensing, with a particular focus on developing rigorous mathematical models for GNSS reflectometry applications.
My research explores unconditional and conditional signal modeling in the context of GNSS-R, a field that still offers significant room for theoretical and methodological development, especially on the reflectometry side. During the first stage of my PhD, I focused on models based on Gaussian assumptions for both the reflection coefficient and the noise. These models provide a solid framework for deriving estimators, performance bounds, and deeper insight into the statistical structure of the signals involved.
A central objective of my work is to improve the understanding of delay and random scattering estimation in GNSS-R systems. This research has already led to two conference papers during my first year. My first paper, “Delay and Random Scattering Estimation with a Band-Limited Signal: Unconditional CRB and MLE,” was accepted at ICASSP, one of the leading conferences in signal processing. My second paper, “On the Delay and Gaussian Random Scattering Estimation for GNSS-R Applications,” was submitted to IGARSS, a major conference in remote sensing.
Looking ahead, I aim to extend this work beyond Gaussian models in order to study more realistic scenarios where the normality assumptions may not hold. Another important direction is the extension of current models to multi-receiver configurations, which aligns with the long-term vision of the project: systems involving many receivers, similar to a swarm of CubeSats. This perspective opens the door to new challenges in distributed estimation, large-scale sensing, and future spaceborne remote sensing systems.
As part of my PhD, I will also take part in an international collaboration in Barcelona in September 2026, under the supervision of Estel Cardellach. Her group’s expertise in the physical effects of GNSS-R will complement the strong mathematical and statistical foundation developed at SUPAERO, creating a valuable connection between theory and physical modeling.
Through this PhD, I aim to contribute to the advancement of GNSS-R by combining rigorous statistical methods with practical remote sensing challenges, helping to build the foundations for future innovative Earth observation systems.
