Skip to Main content Skip to Navigation
Conference papers

Adaptive detection of a Gaussian signal in Gaussian noise

Abstract : Adaptive detection of a Swerling I-II type target in Gaussian noise with unknown covariance matrix is addressed in this paper. The most celebrated approach to this problem is Kelly’s generalized likelihood ratio test (GLRT), derived under the hypothesis of deterministic target amplitudes. While this conditional model is ubiquitous, we investigate here the equivalent GLR approach for an unconditional model where the target amplitudes are treated as Gaussian random variables at the design of the detector. The GLRT is derived which is shown to be the product of Kelly’s GLRT and a corrective, data dependent, term. Numerical simulations are provided to compare the two approaches.
Keywords : Detection
Document type :
Conference papers
Complete list of metadata

Cited literature [7 references]  Display  Hide  Download
Contributor : Open Archive Toulouse Archive Ouverte (OATAO) Connect in order to contact the contributor
Submitted on : Friday, December 9, 2016 - 11:16:54 AM
Last modification on : Wednesday, January 31, 2018 - 2:02:07 PM
Long-term archiving on: : Thursday, March 23, 2017 - 9:32:58 AM


Files produced by the author(s)


  • HAL Id : hal-01413003, version 1
  • OATAO : 16661


Olivier Besson, Eric Chaumette, François Vincent. Adaptive detection of a Gaussian signal in Gaussian noise. CAMSAP 2015, Dec 2015, Cancun, Mexico. pp. 117-120. ⟨hal-01413003⟩



Record views


Files downloads