A persymmetric modified-SMI algorithm

Lujing Cai, Hong Wang

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


By exploring the covariance structure information to reduce the uncertainty in adaptive processing, this paper presents a Persymmetric Modified Sample Matrix Inversion algorithm (PMSMI), together with the closed-form expressions of probabilities of detection and false larm. The new algorithm, which requires less computation and has a Constant False Alarm Rate (CFAR) feature, can significantly outperform the corresponding unstructured MSMI, especially in a severely non-stationary/non-homogeneous interference environment.

Original languageEnglish (US)
Pages (from-to)27-34
Number of pages8
JournalSignal Processing
Issue number1
StatePublished - Apr 1991


  • Adaptive detection
  • doubly symmetric covariance

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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