This paper presents a neural-enhanced probabilistic model and corresponding factor graph-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The introduced hybrid probabilistic model consists of physics-based and data-driven measurement models capturing the information contained in both, the line-of-sight (LOS) component as well as in multipath components (NLOS components). The physics-based and data-driven models are embedded in a joint Bayesian framework allowing to derive from first principles a factor graph-based algorithm that fuses the information of these models.
Date: 7 June 2024
Chapter: Tainan Chapter
Chapter Chair: Mong F. Horng
Title: Augmented/Mixed Reality Audio for Hearables: Sensing, Control and Rendering
Mehrnaz Shokrollahi is passionate about technology and AI, with a special focus on championing women in these fields. Currently, she serves as the Director of Data Science & Advanced Analytics at the Royal Bank of Canada (RBC).