In the last few years, the compressed sensing (CS) theory, which has been introduced in Refs. 7 and 8, indicates that one can stably and accurately reconstruct nearly sparse signals from dramatically undersampled data in an incoherent domain. With this prominent advantage, the CS theory has been used in wide-ranging applications. Meanwhile, the idea of CS has also been introduced in imaging radar system.9–15 In 10, a CS-based radar imaging method is proposed in which the pulse compression matched filter is no longer needed. In 11, a random-frequency SAR imaging scheme based on CS is put forward. The advantage of this method is that only a small number of random frequencies are needed to reconstruct the image of the targets. However, it does not consider the situation that some of SAR echo data may be lost in the coherent integration time. In 12, the CS-based imaging algorithm is proposed to obtain the super-resolving imaging result with full data for stepped-frequency SAR. Also, the sparse recovery method is utilized to estimate the range and Doppler of multiple targets with random stepped-frequency radar.1314 proposes a step-frequency radar system based on compressive sampling, which can use a smaller bandwidth to achieve a high range and speed resolution. If a part of the subpulses of SFWs is randomly selected for transmission, the interference frequency band will be avoided and the time period to transmit will be shortened,15 which can improve the antijamming capability, increase the equivalent pulse repetition frequency (PRF), and restrain the azimuth ambiguity. Thus, a sparse SFW for SAR imaging is studied in this paper.