# Subspace Tracking with Dynamical Models on the Grassmannian

arXiv: https://arxiv.org/abs/2402.10352 · HTML: https://mlsanity.com/paper/2402.10352/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2024.

## Built on by (most cited first, 2 of 2)

- [Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation](https://mlsanity.com/paper/2505.10882/) (2025)
- [Random features for Grassmannian kernel approximation with bounded rank-one projections](https://mlsanity.com/paper/2608.04227/) (2026)

## Abstract (arXiv)

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.