Low-complexity sparse reconstruction for high-resolution multi-static passive SAR imaging

Bistatic passive synthetic aperture radar (SAR) systems using ground broadcast and wireless network signals suffer from low spatial resolution due to the narrow bandwidths and low carrier frequencies. By exploiting multiple distributed illuminators, multi-static passive radar has the possibility of producing high-resolution SAR images. In this paper, a two-stage image formation approach, which combines the Fourier transform and sparse reconstruction strategies, is proposed to process multi-static SAR images. This method exploits the group sparsity of the sparse scene, i.e., the observations associated with different bistatic pairs share the same support of the sparse scene but correspond to aspect-dependent scattering coefficients. Such observations are described as a number of generally disjoint sub-bands in the two-dimensional spatial frequency domain. In each sub-band, the sampling satisfies the Nyquist criterion, whereas different sub-bands are sparsely distributed. In the proposed approach, Fourier-based reconstruction is applied to produce the coarse resolution images, which are then combined to produce a high-resolution image through the exploitation of sparse reconstruction techniques. The proposed approach greatly improves the imaging quality as compared to Fourier-based reconstruction, whereas it exhibits significant reduction of the computational complexity when compared to direct application of sparse reconstruction techniques. The exploitation of block sparsity-based techniques also permits practical treatment of the angle-dependent target scattering characteristics in SAR image construction. The advantages of the proposed approach are delineated using analysis and simulations. * Part of this work was presented at the 2014 IEEE Radar Conference [1].