Multiband Passive ISAR Processing Based on Bayesian Compressive Sensing

Ran Zhang, Xia Bai, Juan Zhao

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

4 Citations (Scopus)

Abstract

In this paper, ISAR (Inverse Synthetic Aperture Radar) imaging for multiband passive bistatic radar (PBR) is investigated. To reach finer range resolution without the degradations of the image, we present a novel P-ISAR processing scheme based on Bayesian compressive sensing (CS) that considers the relationship between spectral structure of multichannel signals and measurement matrix. First, sparse representation of PBR signal in the range direction is given by analyzing cross correlation of echo. Then a novel recovery algorithm named as WBSBL (Weighted Block Sparse Bayesian Learning) is proposed and used to the passive ISAR processing scheme. Simulation results show the effectiveness of the proposed scheme which can guarantee the image quality.

Original languageEnglish
Title of host publicationICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728123455
DOIs
Publication statusPublished - Dec 2019
Event2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019 - Chongqing, China
Duration: 11 Dec 201913 Dec 2019

Publication series

NameICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019

Conference

Conference2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Country/TerritoryChina
CityChongqing
Period11/12/1913/12/19

Keywords

  • ISAR
  • Passive radar
  • compressive sensing
  • range profile
  • sparse Bayesian recovery

Fingerprint

Dive into the research topics of 'Multiband Passive ISAR Processing Based on Bayesian Compressive Sensing'. Together they form a unique fingerprint.

Cite this