Abstract
Recently, the sparse reconstruction algorithms (SRAs) based on compressive sensing (CS) have been applied in the fields of synthetic aperture radar (SAR) imaging and show plenty of potential advantages. However, due to the great computational complexity and memory cost caused by matrix-vector multiplications, most of these algorithms are not suitable to reconstruct large-scale observed scenes. To solve this problem, we construct a backprojection based imaging operator, and introduce it to the complex approximate message passing algorithm (CAMP). The new image formation algorithm is called BP-CAMP in this paper. Compared with the approximated observation methods deduced from the FFT-based imaging technology, BP-CAMP is not limited by observation models of the radar and motion modes of the platform, and it therefore possesses universal applicability. By the simulations and real data processing, the experimental results show that BP-CAMP has lower computational complexity and memory cost than CAMP, and also achieves SAR imaging with under-sampled echo data.
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