Improved orthogonal projection adaptive beamforming by using reconstructed interference covariance matrix

Xiaopeng Yang, Lu Yan, Zongao Zhang, Yuze Sun, Teng Long

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

1 Citation (Scopus)

Abstract

When the desired signal is present in the training snapshots, the performance of conventional orthogonal projection (OP) adaptive beamforming degrades severely due to the desired signal cancellation effect. To overcome this deficiency, the improved orthogonal projection (IOP) adaptive beamforming by using reconstructed interference covariance matrix is proposed. In the proposed algorithm, the interference covariance matrix is firstly reconstructed by integrating the Capon spatial spectrum over a region separated from the desired signal direction. Subsequently, the jammer subspace is estimated, and then the adaptive weight vector is calculated using conventional OP algorithm. The simulation results show that the corresponding output signal-to-jammer-plus-noise ratio (SJNR) performance of proposed IOP algorithm is almost same with the optimum beamformer with the desired signal in the training snapshots. Therefore, the proposed IOP algorithm is significantly effective for the actual system.

Original languageEnglish
Title of host publication2014 International Radar Conference, Radar 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479941957
DOIs
Publication statusPublished - 12 Mar 2014
Event2014 International Radar Conference, Radar 2014 - Lille, France
Duration: 13 Oct 201417 Oct 2014

Publication series

Name2014 International Radar Conference, Radar 2014

Conference

Conference2014 International Radar Conference, Radar 2014
Country/TerritoryFrance
CityLille
Period13/10/1417/10/14

Keywords

  • Adaptive beamforming
  • covariance matrix
  • desired signal cancellation
  • orthogonal projection
  • spatial spectrum

Fingerprint

Dive into the research topics of 'Improved orthogonal projection adaptive beamforming by using reconstructed interference covariance matrix'. Together they form a unique fingerprint.

Cite this