Using Compressive Imaging as a Fast Class Formation Method in Automatic Target Acquisition

摘要

Subspace projection is an effective and established way to form classes in the Automatic Target Acquisition (ATA) problem. Class subspace formation is viewed in this paper as an over specified Fh = u problem. Recent advances in compressive imaging show that this problem can be solved for sparse matrices via iterative techniques. Convergence of these techniques is aided by a metric induced by an appropriately selected norm. In this paper we will use infrared data to show this rapid class formation and to compare convergence for two norms. Based on this class formulation a new method for ATA solution will also be demonstrated.