  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.
