In this paper, we address imaging of the interior structure of a building using a reduced number of measurements in through-the-wall imaging and urban sensing applications. Unlike majority of the feature detection methods that are applied in the image domain, the proposed approach works in the data domain and exploits prior information of construction practices together with the sparsity described by the features. More specifically, the interior walls are assumed to be either parallel or perpendicular to the front wall and a dictionary of possible wall locations is proposed as a sparse representation of the scene. Compressive sensing is then applied to the reduced set of observations to recover the true positions of the walls. Supporting results based on laboratory experiments are provided.

Peer Reviewed