Invariance theory for adaptive detection in interference with group symmetric covariance matrix

Antonio De Maio*, Danilo Orlando, Ilya Soloveychik, Ami Wiesel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


This paper describes the adaptive detection of point-like targets in a Gaussian environment assuming a group symmetric structure for the interference covariance matrix. This special configuration enforces block-sparsity and permits splitting of the original observation space into lower-dimensional subspaces, each characterized by its own nuisance parameters. Hence, the Principle of Invariance is invoked to select decision rules enjoying some symmetry defined by a suitable group of transformations which leave the decision problem unaltered. All the invariant tests can be expressed as functions of a maximal invariant statistic whose derivation is among the key results of this paper. Finally, some common design criteria are applied to come up with adaptive architectures that share invariance, and their behavior is assessed to highlight the interplay between sample size and detection performance in comparison with conventional decision schemes.

Original languageAmerican English
Article number7513456
Pages (from-to)6299-6312
Number of pages14
JournalIEEE Transactions on Signal Processing
Issue number23
StatePublished - 1 Dec 2016

Bibliographical note

Publisher Copyright:
© 1991-2012 IEEE.


  • Adaptive detection
  • GLRT
  • Rao test
  • Wald test
  • group symmetric structures
  • invariance


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