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To achieve high-resolution 2-D images, through-wall imaging (TWI) radar with ultra-wideband and long antenna arrays faces considerable technical challenges such as a prolonged data collection time, a huge amount of data, and a high hardware complexity. This paper presents a novel data acquisition scheme and an imaging algorithm for TWI radar based on compressive sensing (CS), which states that a signal having a sparse representation can be reconstructed from a small number of nonadaptive randomized projections by solving a tractable convex program. Instead of measuring all spatial-frequency data, a few samples, by employing an overcomplete dictionary, are sufficient to obtain reliable target space images even at high noise levels. Preliminary simulated and experimental results show that the proposed algorithm outperforms the conventional delay-and-sum beamforming method even though many fewer CS measurements are used.