No abstract

No abstract

No abstract

The dual-channel synthetic aperture radar (SAR) system is widely applied in the field of ground moving-target indication (GMTI). With the increase of the imaging resolution, the resulting substantial raw data samples increase the transmission and storage burden. We tackle the problem by adopting the joint sparsity model 1 (JSM-1) in distributed compressed sensing (DCS) to exploit the correlation between the two channels of the dual-channel SAR system. We propose a novel algorithm, namely the hierarchical variational Bayesian based distributed compressed sensing (HVB-DCS) algorithm for the JSM-1 model, which decouples the common component from the innovation components by applying variational Bayesian approximation. Using the proposed HVB-DCS algorithm in the dual-channel SAR based GMTI (SAR-GMTI) system, we can jointly reconstruct the dual-channel signals, and simultaneously detect the moving targets and stationary clutter, which enables sampling at a further lower rate in azimuth as well as improves the reconstruction accuracy. The simulation and experimental results show that the proposed HVB-DCS algorithm is capable of detecting multiple moving targets while suppressing the clutter at a much lower data rate in azimuth compared with the compressed sensing (CS) and range-Doppler (RD) algorithms.

We consider the joint sparsity Model 1 (JSM-1) in a decentralized scenario, where a number of sensors are connected through a network and there is no fusion center. A novel algorithm, named distributed compact sensing matrix pursuit (DCSMP), is proposed to exploit the computational and communication capabilities of the sensor nodes. In contrast to the conventional distributed compressed sensing algorithms adopting a random sensing matrix, the proposed algorithm focuses on the deterministic sensing matrices built directly on the real acquisition systems. The proposed DCSMP algorithm can be divided into two independent parts, the common and innovation support set estimation processes. The goal of the common support set estimation process is to obtain an estimated common support set by fusing the candidate support set information from an individual node and its neighboring nodes. In the following innovation support set estimation process, the measurement vector is projected into a subspace that is perpendicular to the subspace spanned by the columns indexed by the estimated common support set, to remove the impact of the estimated common support set. We can then search the innovation support set using an orthogonal matching pursuit (OMP) algorithm based on the projected measurement vector and projected sensing matrix. In the proposed DCSMP algorithm, the process of estimating the common component/support set is decoupled with that of estimating the innovation component/support set. Thus, the inaccurately estimated common support set will have no impact on estimating the innovation support set. It is proven that under the condition the estimated common support set contains the true common support set, the proposed algorithm can find the true innovation set correctly. Moreover, since the innovation support set estimation process is independent of the common support set estimation process, there is no requirement for the cardinality of both sets; thus, the proposed DCSMP algorithm is capable of tackling the unknown sparsity problem successfully.