High-resolution ISAR imaging with sparse subband based on waveform fusion dictionary

Juntao Ma, Meiguo Gao, Mingfei Xia, Wenhua Hu, Zizhi Gao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In this paper, a new high-resolution ISAR Imaging method by using sparse subband measurements is developed. It requires no resampling the irregularly measurements onto a uniform frequency grid. Firstly, a one-dimensional waveform dictionary for LFM signal after dechirping is constructed, and the principle of dictionary fusion is illustrated. Then, the two-dimensional waveform fusion dictionary is proposed. Secondly, the fusion imaging method based on Bayesian framework is analyzed, and a hierarchical form of the Laplace prior is used to sparse modeling of the high-resolution ISAR image. Finally, we provide experimental results with one-dimensional and two-dimensional fusion imaging, which illustrated the effectiveness and the superiority of the proposed fusion method over the existing algorithms.

Original languageEnglish
Title of host publicationProceedings of 2017 IEEE 7th International Conference on Electronics Information and Emergency Communication, ICEIEC 2017
EditorsLi Wenzheng, Seng-Pan U, Zhu Hongdan, Ni Shaowen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages385-390
Number of pages6
ISBN (Electronic)9781509030248
DOIs
Publication statusPublished - 19 Oct 2017
Event7th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2017 - Macau, China
Duration: 21 Jul 201723 Jul 2017

Publication series

NameProceedings of 2017 IEEE 7th International Conference on Electronics Information and Emergency Communication, ICEIEC 2017

Conference

Conference7th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2017
Country/TerritoryChina
CityMacau
Period21/07/1723/07/17

Keywords

  • Bayesian
  • High-resolution
  • ISAR
  • Signal-Fusion

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