A Sparsity-Based Passive Multistatic Detector

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

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

In this paper, we examine the problem of target detection for the multistatic passive radar. Passive radar systems leverage the existing wireless sources, such as radio/TV stations and cellular signals that are referred to as illuminators of opportunity (IOs), to illuminate the environment and provide surveillance functions. Usually, these IO source signals are sparse or locally sparse in the frequency domain. We develop a passive multistatic detector by exploiting the sparsity or local sparsity of the IO signals. To improve the computational efficiency, two fast implementations of the proposed detector are also introduced. Simulation results show that the proposed approaches outperform the conventional passive detection methods that model the IO signals as unknown without any specific structures.

Original languageEnglish
Article number8631164
Pages (from-to)3658-3666
Number of pages9
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume55
Issue number6
DOIs
StatePublished - Dec 2019

Keywords

  • Generalized likelihood ratio test (GLRT)
  • multistatic passive radar
  • sparse signal
  • target detection

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