Improving Inference for Spatial Signals by Contextual False Discovery Rates

Martin Gölz*, Abdelhak M. Zoubir, Visa Koivunen

*Corresponding author for this work

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

1 Citation (Scopus)
7 Downloads (Pure)

Abstract

A spatial signal is monitored by a large-scale sensor network. We propose a novel method to identify areas where the signal behaves interestingly, anomalously, or simply differently from what is expected. The sensors pre-process their measurements locally and transmit a local summary statistic to a fusion center or a cloud. This saves bandwidth and energy. The fusion center or cloud computes a spatially varying empirical Bayes prior on the signal's spatial behavior. The spatial domain is modeled as a fine discrete grid. The contextual local false discovery rate is computed for each grid point. A decision on the local state of the signal is made for each grid point, hence, many decisions are made simultaneously. A multiple hypothesis testing approach with false discovery rate control is used. The proposed procedure estimates the areas of interesting signal behavior with higher precision than existing methods. No tuning parameters have to be defined by the user.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PublisherIEEE
Pages5967-5971
Number of pages5
ISBN (Electronic)9781665405409
DOIs
Publication statusPublished - 2022
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Singapore, Singapore
Duration: 23 May 202227 May 2022

Publication series

NameIEEE International Conference on Acoustics, Speech and Signal Processing
Volume2022-May
ISSN (Print)1520-6149

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
Country/TerritorySingapore
CitySingapore
Period23/05/202227/05/2022

Keywords

  • information fusion
  • local false discovery rate
  • multiple hypothesis testing
  • Sensor networks
  • spatial inference

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