Abstract
A sparse-based refocusing methodology for multiple slow-moving targets (MTs) located inside strong clutter regions is proposed in this paper. The defocused regions of MTs in synthetic aperture radar (SAR) imagery were utilized here instead of the whole original radar data. A joint radar projection operator for the static and moving objects was formulated and employed to construct an optimization problem. The Lp norm constraint was utilized to promote the separation of MT data and the suppression of clutter. After the joint sparse imaging processing, the energy of strong static targets could be suppressed significantly in the reconstructed MT imagery. The static scene imagery could be derived simultaneously without the defocused MT. Finally, numerical simulations were used verify the validity and robustness of the proposed methodology.
Highlights
Synthetic aperture radar (SAR) [1], which is an advanced remote sensing system, has been widely used in the past few decades
When there are multiple moving targets (MTs) in the illuminated scenery, smearing and geometry position deviation [2,3] generally emerge in the constructed imagery
Dopplerwhen characteristics of separate orby suppress the clutter in advance. This isthe difficult to realize the smeared radar data from the static and moving objects, we describe the conversion of the MT overlaps with the static targets in the imagery domain
Summary
Synthetic aperture radar (SAR) [1], which is an advanced remote sensing system, has been widely used in the past few decades. A range frequency reversal transform-fractional Fourier transform (RFRT-FrFT) [19] has recently been developed for MT range cell migration correction (RCMC) after DPCA They had to consider all the possible values of moving parameters in Rodrigo and Wang [17] and the performance of the RFRT-FrFT method will be affected by the existence of cross-terms and strong clutter. To promote the separation of data and the suppression of artifacts and side lobes, we consider employing a sparse constraint on the solution This has been utilized in compressed sensing (CS) [24,25] to realize SAR or inverse synthetic aperture radar (ISAR) imaging [26,27,28,29,30], tomography, and ground moving target indication in the past few decades. Numerical simulation verifies that the algorithm can implement multiple moving target imaging conveniently
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