Adaptive signal detection in subspace interference with partial prior knowledge

Yuan Jiang, Hongbin Li, Jun Fang, Muralidhar Rangaswamy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We consider the problem of weak signal detection in strong disturbance with a subspace structure. Unlike conventional subspace detection techniques relying on the availability of a large amount of training data, we consider a knowledge-aided (KA) subspace detection approach for training limited scenarios by incorporating partial prior knowledge of the subspace. A unique feature of the proposed approach is that it can identify the missing subspace bases and recover the full subspace structure by using only the test signal, thus bypassing the need for training data. The proposed approach utilizes a Bayesian hierarchical model for knowledge representation. The model is integrated within a sparse Bayesian framework, which promotes parsimonious subspace representation of the observed data. A variational Bayesian inference algorithm is developed based on the proposed model to recover parameters and subspace structures associated with the disturbance, which are then brought into a generalized likelihood ratio test (GLRT) to perform signal detection. Numerical results are presented to illustrate the performance of the proposed subspace detector in comparison with several notable existing methods.

Original languageEnglish
Title of host publication2017 IEEE Radar Conference, RadarConf 2017
Pages897-902
Number of pages6
ISBN (Electronic)9781467388238
DOIs
StatePublished - 7 Jun 2017
Event2017 IEEE Radar Conference, RadarConf 2017 - Seattle, United States
Duration: 8 May 201712 May 2017

Publication series

Name2017 IEEE Radar Conference, RadarConf 2017

Conference

Conference2017 IEEE Radar Conference, RadarConf 2017
Country/TerritoryUnited States
CitySeattle
Period8/05/1712/05/17

Keywords

  • Bayesian interference
  • Knowledge-aided processing
  • Radar applications
  • Subspace signal detection

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