In this paper, basis pursuit denoising (BPDN) is applied to extract three-dimensional scattering centers of complex radar targets. Since the distributions of scattering centers are usually sparse in the high-frequency optics region, the valid backscattering coefficients of radar targets can be obtained with the undersampled measurements in azimuth and elevation. It significantly reduces the time for measuring echo signals, which promotes the efficiency of radar systems. However, due to the great computation load and memory cost caused by the matrix-vector products, most of the sparse reconstruction algorithms are not suitable for the process of scattering center extraction. To solve this problem, we derive accelerated operators on the basis of support set and filtered backprojection. By combining the sparse solver SPGL1 with the proposed operators, the new algorithm not only reduces the consumption of system resources, but also estimates the backscattering coefficients accurately. Additionally, the experimental results and analysis demonstrate that the proposed technique possesses high data compression ratio and small radar cross section (RCS) reconstruction error.

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.

The displaced phase center antenna (DPCA) technique is a classical method for achieving high-resolution wide-swath synthetic aperture radar (SAR) imaging. For optimum performance, the pulse repetition frequency (PRF) of DPCA SAR systems should satisfy the azimuth uniform sampling condition as far as possible. However, this rigid PRF selection may conflict with the timing diagram for some incidence angles, which usually results in a nonuniform sampling of the synthetic aperture. According to the sparse signal processing theory, this letter proposes a novel DPCA imaging algorithm for the nonuniform displaced phase center sampling. By combining the DPCA data processing operator based on a filter bank with the ℓqregularization scheme, the algorithm can efficiently recover the backscattering coefficients of the observed scene. The experimental results have shown that it is capable of resolving ambiguity and suppressing clutter effectively and is meanwhile insensitive to additive noise.

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.