A 3D Geometry Reconstruction Method for Space Targets Utilizing Improved ISAR Image Sequence Energy Accumulation

Zhou, Zuobang; Du, Rongzhen; Liu, Lei; Zhou, Feng

doi:10.1109/radar53847.2021.10027901

Cited by 2 publications

(1 citation statement)

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“…However, ISEA necessitates the computation of the imaging plane of space targets, contingent on the radar’s line of sight (LOS) orientation and attitude of the space targets, thereby restricting its applicability to tri-axially stable targets in space. Subsequently, Zuobang Zhou tackled the limitation of ISEA’s inability to reconstruct three-axis unstable targets [ 31 , 32 , 33 ]. Zhou’s method employs the quantum-behaved particle swarm optimization (QPSO) algorithm to estimate the rotational motion parameters of slow rotating space targets (SRSTs), facilitating successful reconstruction.…”

Section: Introductionmentioning

confidence: 99%

PP-ISEA: An Efficient Algorithm for High-Resolution Three-Dimensional Geometry Reconstruction of Space Targets Using Limited Inverse Synthetic Aperture Radar Images

Wang,

Zhu,

et al. 2024

Sensors

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As the variety of space targets expands, two-dimensional (2D) ISAR images prove insufficient for target recognition, necessitating the extraction of three-dimensional (3D) information. The 3D geometry reconstruction method utilizing energy accumulation of ISAR image sequence (ISEA) facilitates superior reconstruction while circumventing the laborious steps associated with factorization methods. Nevertheless, ISEA’s neglect of valid information necessitates a high quantity of images and elongated operation times. This paper introduces a partitioned parallel 3D reconstruction method utilizing sorted-energy semi-accumulation with ISAR image sequences (PP-ISEA) to address these limitations. The PP-ISEA innovatively incorporates a two-step search pattern—coarse and fine—that enhances search efficiency and conserves computational resources. It introduces a novel objective function ‘sorted-energy semi-accumulation’ to discern genuine scatterers from spurious ones and establishes a redundant point exclusion module. Experiments on the scatterer model and simulated electromagnetic model demonstrate that the PP-ISEA reduces the minimum image requirement from ten to four for high-quality scatterer model reconstruction, thereby offering superior reconstruction quality in less time.

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Section: Introductionmentioning

confidence: 99%