Mattia Brambilla

Papers

Total Citations

H-Index

About

Mattia Brambilla is a leading researcher in the field of industrial indoor positioning and wireless localization, with a focus on enabling robust location-based services for autonomous robotics and industrial automation. His major contributions center on developing advanced filtering and sampling techniques to overcome the challenges of mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, which are critical for reliable positioning in complex industrial settings. Notably, his work on the "Switching Model Stein Variational Sampling Filter" (2025) introduces a novel approach to handling dynamic channel conditions, enhancing the accuracy and resilience of Ultra Wide-Band (UWB) and 5G-advanced millimeter-wave systems. With over 2 citations for this recent paper, Brambilla’s research is gaining traction for its practical impact on real-world IoT applications, including augmented and virtual reality. His achievements demonstrate a deep understanding of statistical signal processing and Bayesian inference, positioning him as a key contributor to the next generation of industrial indoor positioning systems.