학술논문

Subspace Tracking with Dynamical Models on the Grassmannian
Document Type
Working Paper
Source
Subject
Electrical Engineering and Systems Science - Signal Processing
Language
Abstract
Tracking signals in dynamic environments presents difficulties in both analysis and implementation. In this work, we expand on a class of subspace tracking algorithms which utilize the Grassmann manifold -- the set of linear subspaces of a high-dimensional vector space. We design regularized least squares algorithms based on common manifold operations and intuitive dynamical models. We demonstrate the efficacy of the approach for a narrowband beamforming scenario, where the dynamics of multiple signals of interest are captured by motion on the Grassmannian.
Comment: Submitted to IEEE SAM 2024. This work has been submitted to the IEEE for possible publication