Target detection above rough surfaces in microwave imaging using Compressive Sampling

A subspace extraction approach for detection of targets embedded in the clutter is presented in this work. Subspace extraction approach that makes use of both Compressive Sampling and Principal Component Analysis (PCA) is presented in this paper. Inverse Synthetic Aperture Radar (ISAR) Imaging measurement data is used to validate the proposed approach. Experimental results of targets above rough surface with intermediate roughness are presented. Results showed the dimensionality of an intermediate scale rough surface is generally larger than the dimensionality of the finite size targets. Results showed that by compressing the dimensionality through compressive sampling and extracting the principal components, significant improvement in target subspace extraction can be achieved.