Distributed Compressed Sensing Based Ground Moving Target Indication for Dual-Channel SAR System

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.

Staring Spotlight SAR with Nonlinear Frequency Modulation Signal and Azimuth Non-Uniform Sampling for Low Sidelobe Imaging

“…After circular shifting and windowing, the linear unbiased minimum variance is used to calculate the phase error gradient after the inverse Fourier transform, and the well focused SAR images are output while the phase error is less than the designed error threshold. The major disadvantage of the BPA is low computational efficiency, while frequency domain algorithms [25][26][27] show more computational efficiency and are widely adopted in recent SAR imaging modes. However, to handle the raw data of the new proposed modes, these algorithms usually need to be modified.…”

“…As shown in Figure 8, the range preprocessing step includes NLFM pulse compression and the first-order MoCo. In airborne SAR imaging, MoCo must be considered to The major disadvantage of the BPA is low computational efficiency, while frequency domain algorithms [25][26][27] show more computational efficiency and are widely adopted in recent SAR imaging modes. However, to handle the raw data of the new proposed modes, these algorithms usually need to be modified.…”

“…where ∆t is the original average azimuth time sampling interval. The major disadvantage of the BPA is low computational efficiency, while frequency domain algorithms [25][26][27] show more computational efficiency and are widely adopted in recent SAR imaging modes. However, to handle the raw data of the new proposed modes, these algorithms usually need to be modified.…”

SAR Multi-Angle Observation Method for Multipath Suppression in Enclosed Spaces

“…However, these methods demand significant data and parameter input and are characterized by high computational complexity. Compressed sensing methods are revolutionizing image reconstruction by overcoming the limitations of the traditional Nyquist sampling theorem [20][21][22]. In certain applications, these methods not only reduce hardware complexity and cost but also increase resolution.…”