A compressive sensing imaging algorithm for millimeter-wave synthetic aperture imaging radiometer in near-field

In order to overcome the disadvantage of large data amount and receiver number in near-field passive millimeter-wave synthetic aperture imaging radiometer (PMSAIR), an imaging algorithm based on Compressive Sensing (CS) theory is proposed in this paper. Due to the fact that the brightness temperature distributions of the observed target have a sparse representation in some proper transform domain (such as the spatial finite-differences and wavelet coefficients), we use the CS approach to reconstruct the brightness temperature images from very few visibilities. Thus the amount of data and number of receivers can be further reduced than those traditional methods based on the Fourier transform. The reconstruction is performed by minimizing the Total-Variation norm of brightness temperature image. Finally, the numerical simulation of synthetic aperture imaging demonstrates that the proposed algorithm is an efficient, feasible imaging algorithm for near-field PMSAIR.