摘要： This paper introduces a sparse scene reconstruction algorithm for automobile frequency-modulated continuous-wave synthetic aperture radar (FMCW SAR) through scaled compressed sensing (CS). An FMCW radar leads to low manufacturing cost, compact realization, and low transmission power. An automobile SAR is more economical and easier to implement than typical SAR platforms (e.g., satellites and aircraft). We apply CS to randomly subsampled raw data of automobile FMCW SAR for sparse reconstruction. We exploit the fact that the velocity of an automobile is significantly lower than that of the SAR platforms, which leads to a vastly narrow bandwidth of an azimuth-matched filter in the azimuth compression of the range-Doppler algorithm (RDA). Low-frequency azimuth data have a fundamental effect on azimuth compression. We propose a new reconstruction scheme, scaled CS, which specializes in low-frequency information recovery for automobile SAR. The scheme is based on basis pursuit denoising (BPDN). A Ku-band FMCW SAR system is developed to verify the performance of the proposed algorithm. We mount our system on an automobile and collect FMCW SAR raw data in the stripmap mode with a van maintained a constant speed on a highway. The proposed reconstruction algorithm shows improved recovery performance for automobile FMCW SAR, which is validated by processing a high-resolution real SAR image.