Due to the great computation load and memory cost of the matrix-vector multiplication, the sparse reconstruction algorithms are severely limited in the applications of radar imaging with real data. In order to solve this problem, we construct a backprojection-based range–azimuth decoupled operator (BP-RADOp) and combine the complex approximate message passing algorithm (CAMP) with it. We call this algorithm BP-CAMP in this letter. Since BP-RADOp retains the merits of the backprojection method entirely (i.e., perfect motion compensation for any flight path, precise focus for arbitrarily wide bandwidths and integration angles, low artifact levels, unlimited scene size, and strictly local processing), it has universal applicability in comparison with the other decoupled operators deduced from the fast Fourier transform-based image formation algorithms. The theoretical analysis indicates when BP-CAMP and CAMP are both used to reconstruct large-scale observed scenes; the former has lower computation load and memory cost than the latter. Meanwhile, it is demonstrated that BP-CAMP achieves high-quality synthetic aperture radar imaging with undersampled echo data, and it is as robust as CAMP to additive noise by the simulations and real data processing.
['Xiangyin Quan', 'Bingchen Zhang', 'Jian Guo Liu', 'Yirong Wu']
An Efficient General Algorithm for SAR Imaging: Complex Approximate Message Passing Combined With Backprojection