Multiple Hypothesis Testing Framework for Spatial Signals

Martin Golz, Abdelhak M. Zoubir, Visa Koivunen

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)


The problem of identifying regions of spatially interesting, different or adversarial behavior is inherent to many practical applications involving distributed multisensor systems. In this work, we develop a general framework stemming from multiple hypothesis testing to identify such regions. A discrete spatial grid is assumed for the monitored environment. The spatial grid points associated with different hypotheses are identified while controlling the false discovery rate at a pre-specified level. Measurements are acquired using a large-scale sensor network. We propose a novel, data-driven method to estimate local false discovery rates based on the spectral method of moments. Our method is agnostic to specific spatial propagation models of the underlying physical phenomenon. It relies on a broadly applicable density model for local summary statistics. In between sensors, locations are assigned to regions associated with different hypotheses based on interpolated local false discovery rates. The benefits of our method are illustrated by applications to spatially propagating radio waves.

Original languageEnglish
Pages (from-to)771-787
Number of pages16
JournalIEEE Transactions on Signal and Information Processing over Networks
Early online date2022
Publication statusPublished - 2022
MoE publication typeA1 Journal article-refereed


  • Behavioral sciences
  • density estimation
  • Information processing
  • Large-scale inference
  • local false discovery rate
  • method of moments
  • multiple hypothesis testing
  • Probability
  • radial basis function interpolation
  • sensor networks
  • Sensor phenomena and characterization
  • Sensors
  • Testing
  • Wireless sensor networks


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