Moving Target Artifacts in Bistatic Synthetic Aperture Radar Images

Duman, Kaan; Yazıcı, Birsen

doi:10.1109/tci.2015.2440995

Cited by 29 publications

(9 citation statements)

References 44 publications

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“…Eric Mason is with the United States Naval Research Laboratory, 4555 Overlook Ave SW, Washington, DC 20375 USA (e-mail: eric.mason@nrl.navy.mil) Yonina C. Eldar is with the Department of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot 7610001, Israel (e-mail: yonina.eldar@weizmann.ac.il) resulting in artifacts in the reconstructed imagery [4]. If the velocities are known, imaging algorithms can be easily modified to compensate for the range variations.…”

Section: A Motivationmentioning

confidence: 99%

Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization

Thammakhoune

Yonel

Mason

et al. 2021

IEEE Trans. Comput. Imaging

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In this paper, we present an approach for ground moving target imaging (GMTI) and velocity recovery using synthetic aperture radar. We formulate the GMTI problem as the recovery of a phase-space reflectivity (PSR) function which represents the strengths and velocities of the scatterers in a scene of interest. We show that the discretized PSR matrix can be decomposed into a rank-one, and a highly sparse component corresponding to the stationary and moving scatterers, respectively. We then recover the two distinct components by solving a constrained optimization problem that admits computationally efficient convex solvers within the proximal gradient descent and alternating direction method of multipliers frameworks. Using the structural properties of the PSR matrix, we alleviate the computationally expensive steps associated with rank-constraints, such as singular value thresholding. Our optimization-based approach has several advantages over state-of-the-art GMTI methods, including computational efficiency, applicability to dense target environments, and arbitrary imaging configurations. We present extensive simulations to assess the robustness of our approach to both additive noise and clutter, with increasing number of moving targets. We show that both solvers perform well in dense moving target environments, and low-signal-toclutter ratios without the need for additional clutter suppression techniques.

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Section: A Motivationmentioning

confidence: 99%

Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization

Thammakhoune

Yonel

Mason

et al. 2021

IEEE Trans. Comput. Imaging

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“…For the same reason, the clutter nonstationary [4]- [6] is increased and will degrade the performance of traditional suppression methods. The complexity of clutter suppression and imaging processing for moving targets (MT) will also be increased severely [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Sparse Imaging-Based Clutter Suppression and Refocusing of MTs in Passive Bistatic SAR

Xin

Song

2020

IEEE Access

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A sparse-imaging-based methodology for the clutter suppression and refocusing of multiple slow-moving targets (MT) in passive bistatic synthetic aperture radar (SAR) is proposed in this paper. The defocused regions of MTs in the bistatic imagery are utilized here. A joint radar projection operator for the static and moving objects is formulated and employed to construct a source separation problem. The Lp norm and square difference maximization constraint are utilized to promote the separation of MT data and the suppression of clutter. After the joint sparse processing, static-scene imagery and MT imagery can be derived simultaneously. Finally, numerical simulations can verify the validity of the proposed methodology.

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“…Ref. [21] analyzes the signatures of piece-wise constant velocity targets for bistatic collection geometries. Other references are provided in Refs.…”

Section: Introductionmentioning

confidence: 99%