Bayesian subspace recovery with approximate prior knowledge for radar detection

Yuan Jiang, Hongbin Li, Muralidhar Rangaswamy

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

1 Scopus citations

Abstract

We examine the problem of subspace recovery when approximate prior knowledge of the subspace is available. The problem originates from a recent work [1], which presents a subspace knowledge learning (SKL) algorithm for subspace recovery by exploiting partial and partly erroneous prior knowledge of the subspace. However, SKL is limited by the assumption that the subspace bases coincide with some basis vectors of a known overcomplete dictionary matrix. In the presence of grid mismatch, SKL may degrade considerably. Another issue is that SKL is computationally quite intensive, since it is an iterative algorithm involving matrix operations in each iteration. To address these issues, we present herein a modified SKL (mSKL) algorithm for subspace recovery which can exploit approximate prior knowledge of the subspace and cope with the grid mismatch problem. The mSKL algorithm is further integrated with generalized approximate message passing (GAMP) which replaces matrix operations in SKL with scalar approximations and is hence computationally efficient. The resulting subspace recovery algorithm, referred to as the mSKL-GAMP, is used to solve a radar detection problem that involves detecting a multichannel target signal in subspace interference. Numerical results are presented to demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2019 IEEE Radar Conference, RadarConf 2019
ISBN (Electronic)9781728116792
DOIs
StatePublished - Apr 2019
Event2019 IEEE Radar Conference, RadarConf 2019 - Boston, United States
Duration: 22 Apr 201926 Apr 2019

Publication series

Name2019 IEEE Radar Conference, RadarConf 2019

Conference

Conference2019 IEEE Radar Conference, RadarConf 2019
Country/TerritoryUnited States
CityBoston
Period22/04/1926/04/19

Keywords

  • Bayesian inference
  • Knowledge-aided processing
  • Radar signal detection
  • Subspace estimation

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