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.
@article{arxiv.2402.10352,
title = {Subspace Tracking with Dynamical Models on the Grassmannian},
author = {Alex Saad-Falcon and Brighton Ancelin and Justin Romberg},
journal= {arXiv preprint arXiv:2402.10352},
year = {2024}
}
Comments
Submitted to IEEE SAM 2024. This work has been submitted to the IEEE for possible publication