Compressive sensing for radar target detection

Thesis Type: Postgraduate

Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Electrical and Electronics Engineering, Turkey

Approval Date: 2013




Compressive sampling, also known as compressive sensing and sparse recovery, is a new type of sampling theory, which predicts that sparse signals and images can be reconstructed from far less amount of data than what was traditionally considered necessary (i.e. Nyquist/Shannon sampling theory). The theory has many applications such as design of new imaging systems, cameras, sensor networks and analog to digital converters. Several algorithms have been proposed for the measurement and recovery process of the theory. The theory uses only a small amount of measurements which are linear, nonadaptive and suitably designed. The reconstruction process is nonlinear and simply depends on searching for the sparsest vector that is coherent with the measurements. The compressive sensing theory and its key points are explained in detail. In this thesis, compressive sensing (CS) is used to reconstruct the target scene of a radar. The target scene is discretized so that a total of N possible target locations exist. The number of targets K is assumed to be small (i.e., K<