Exploiting the Rules of the TF-MUSIC and Spatial Smoothing to Enhance the DOA Estimation for Coherent and Non-stationary Sources

Ruslan Zhagypar, Kalamkas Zhagyparova, Muhammad Tahir Akhtar

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

3 Scopus citations

Abstract

The paper introduces a combination of several techniques and methods to tackle the problem of Direction-of Arrival (DOA) estimation for coherent and non-stationary signals under severe noise conditions. Until now, these properties of the signals were studied by many researchers, yet the solutions were developed separately. At the first stage of the algorithm, the Spatial Time-Frequency Distribution (STFD) matrix is derived, which accounts for non-stationarity of the signals and allows denoising. The forward-backward spatial smoothing is applied to the STFD matrix to solve the problem of signals coherency. The main principle of the Root MUltiple SIgnal Classification (Root-MUSIC), namely, solving for the roots of the polynomial to obtain the signals DOA is exploited. The algorithm was simulated with coherent chirp signals with different Signal-to Noise Ratio (SNR) values to observe its efficiency compared to the conventional Root-MUSIC and Time-Frequency MUSIC TF-MUSIC) methods.

Original languageEnglish (US)
Title of host publication2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages236-241
Number of pages6
ISBN (Electronic)9789881476883
StatePublished - Dec 7 2020
Event2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Virtual, Auckland, New Zealand
Duration: Dec 7 2020Dec 10 2020

Publication series

Name2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings

Conference

Conference2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020
Country/TerritoryNew Zealand
CityVirtual, Auckland
Period12/7/2012/10/20

Bibliographical note

Publisher Copyright:
© 2020 APSIPA.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Signal Processing
  • Decision Sciences (miscellaneous)
  • Instrumentation

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