Target detection with linear and kernel subspaces matching in the presence of strong clutter

This paper proposes potential approaches to detect the weak target in the presence of strong disturbance. The disturbance consists of strong clutter and white Gaussian noise. The target and clutter are assumed to lie in the corresponding subspaces. The algorithms of subspace matching in the linear and kernel subspaces are derived respectively. The leading eigenvector matching that is the subspace with rank one is investigated as well. The simulation is done for two sensor arrays based on the characteristics of the clutter environment. The results from the simulation show the potential and promising uses of the proposed algorithms to detect the weak target.