Stationary Graph Processes: Nonparametric Spectral Estimation

S Segarra, AG Marques, G. Leus, Alejandro Ribeiro

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

9 Citations (Scopus)

Abstract

Stationarity is a cornerstone property that facilitates the analysis and processing of random signals in the time domain. Although time-varying signals are abundant in nature, in many practical scenarios the information of interest resides in more irregular graph domains. The contribution in this paper is twofold. First, we propose several equivalent notions of weak stationarity for random graph signals, all taking into account the structure of the graph where the random process takes place. Second, we analyze the properties of the induced power spectral density along with nonparametric approaches to estimate it, including average and window-based periodograms.
Original languageEnglish
Title of host publication2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)978-1-5090-2103-1
DOIs
Publication statusPublished - 19 Sep 2016
Event2016 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM - Rio de Janeiro, Brazil
Duration: 10 Jul 201613 Jul 2016
Conference number: 9
https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=35132
http://delamare.cetuc.puc-rio.br/sam2016/index.html

Conference

Conference2016 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM
Abbreviated titleSAM
CountryBrazil
CityRio de Janeiro
Period10/07/1613/07/16
Internet address

Keywords

  • Power spectral density
  • Graph signal processing
  • Weak stationarity
  • Periodogram
  • Windowing

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