A new data acquisition and imaging method exploiting the sparsity of the target space is presented for ground penetrating radar (GPR) imaging. Sparsity is enforced by solving a convex lscr 1  minimization problem which uses a very small number of random measurements. The method can greatly reduce the data acquisition time while producing sparse target space images. Simulation and experimental data results are provided to show that the method has excellent resolution and is robust to noise and random spatial sampling.
['Ali Cafer Gurbuz', 'James H. McClellan', 'Waymond R. Scott']
GPR Imaging Using Compressed Measurements