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      Space Group Debris Imaging Based on Sparse Sample

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          Abstract

          Space group debris imaging is difficult with sparse data in low Pulse Repetition Frequency (PRF) spaceborne radar. To solve this problem in the narrow band system, we propose a method for space group debris imaging based on sparse samples. Due to the diversity of mass, density, and other factors, space group debris typically rotates at a high speed in different ways. We can obtain angular velocity through the autocorrelation function based on the diversity in the angular velocity. The scattering field usually presents strong sparsity, so we can utilize the corresponding measurement matrix to extract the data of different debris and then combine it using the sparse method to reconstruct the image. Furthermore, we can solve the Doppler ambiguity with the measurement matrix in low PRF systems and suppress some energy of other debris. Theoretical analysis confirms the validity of this methodology. Our simulation results demonstrate that the proposed method can achieve high-resolution Inverse Synthetic Aperture Radar (ISAR) images of space group debris in low PRF systems.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 February 2016
          : 5
          : 1
          : 82-89
          Affiliations
          [1 ] National Laboratory of Radar Signal Processing, Xidian University
          [2 ] China Flight Test Establishment
          Article
          2d6736848c28441a89260b064088f05f
          10.12000/JR16012
          3ceb5fd7-ce3c-4aa5-9dce-77ac93cb4473

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          History
          Categories
          Technology (General)
          T1-995

          Remote sensing,Electrical engineering
          Group targets of space debris,Sparse sample,Doppler ambiguity,Auto-correlation function

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