Passive multistatic detection by exploiting a sparsity structure of the IO waveform

Xin Zhang, Fangzhou Wang, Hongbin Li, Johan Sward, Andreas Jakobsson, Braham Himed

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

2 Scopus citations

Abstract

Passive radar employs existing illuminators of opportunity (IOs) to probe the environment and perform surveillance functions. Such IO signals are often sparse or locally sparse in the frequency domain. In this paper, we propose a new target detector for multistatic passive radars with multiple distributed receivers by exploiting the sparsity structure of the IO waveform. Two fast implementations of the proposed detector are derived to improve the computational efficiency. Simulation results show that the proposed approaches outperform conventional passive detection methods that models the IO signals as unknown without any specific structures, especially when the observation data size is limited.

Original languageEnglish
Title of host publication2019 IEEE Radar Conference, RadarConf 2019
ISBN (Electronic)9781728116792
DOIs
StatePublished - Apr 2019
Event2019 IEEE Radar Conference, RadarConf 2019 - Boston, United States
Duration: 22 Apr 201926 Apr 2019

Publication series

Name2019 IEEE Radar Conference, RadarConf 2019

Conference

Conference2019 IEEE Radar Conference, RadarConf 2019
Country/TerritoryUnited States
CityBoston
Period22/04/1926/04/19

Keywords

  • Generalized likelihood ratio test
  • Passive radar
  • Sparsity
  • Target detection

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