Conjugate gradient projection subspace tracking

In this correspondence, we develop a new subspace tracking algorithm called the conjugate gradient projection subspace tracker (CGPST). The algorithm is based on a recently introduced RLS-like subspace cost function, which we recursively minimize using conjugate gradient iterations. Subspace averaging concepts are used to produce an O(r/sup 2/m) algorithm that updates an r-dimensional subspace of C/sup m/. The algorithm is parallelizable, rapidly convergent, numerically stable, and computationally efficient. Simulation studies test the algorithm's performance and show it to compare favorably with other subspace trackers.