Persymmetric Adaptive Array Detection of Spread Spectrum Signals

Jun Liu, Wuyang Zhou, Amir Zaimbashi, Hongbin Li

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The spread spectrum signal detection problem is examined in colored noise with an unknown covariance matrix. When the receiver is equipped with a symmetrically spaced linear array, persymmetry exists in the received data. We exploit the persymmetric structures to design adaptive detectors according to the principles of generalized likelihood ratio test (GLRT), Wald test, and Rao test. It turns out that the proposed GLRT has the same form as the proposed Wald test, and the Rao test does not exist. We prove that the proposed detector exhibits a constant false alarm rate against the unknown noise covariance matrix. Numerical examples demonstrate that the proposed detector has better performance than its non-persymmetric counterpart.

Original languageEnglish
Article number9160984
Pages (from-to)7828-7834
Number of pages7
JournalIEEE Transactions on Information Theory
Volume66
Issue number12
DOIs
StatePublished - Dec 2020

Keywords

  • Rao test
  • Spread spectrum signals
  • Wald test
  • generalized likelihood ratio test
  • persymmetry

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