Robust Sequential Testing of Multiple Hypotheses in Distributed Sensor Networks

Mark R. Leonard; Maximilian Stiefel; Michael Fauß; Abdelhak M. Zoubir

ICASSP 2018accepted0 citations

Robust Sequential Testing of Multiple Hypotheses in Distributed Sensor Networks

Mark R. Leonard, Maximilian Stiefel, Michael Fauß, Abdelhak M. Zoubir

Abstract

The problem of sequential multiple hypothesis testing in a distributed sensor network is considered and two algorithms are proposed: the Consensus + Innovations Matrix Sequential Probability Ratio Test (CIMSPRT for multiple simple hypotheses and the robust Least-Favorable-Density- CIMSPRT for hypotheses with uncertainties in the corresponding distributions. Simulations are performed to verify and evaluate the performance of both algorithms under different network conditions and noise contaminations.

BibTeX

@inproceedings{icassp2018_robustsequential,
  title = {Robust Sequential Testing of Multiple Hypotheses in Distributed Sensor Networks},
  author = {Mark R. Leonard and Maximilian Stiefel and Michael Fauß and Abdelhak M. Zoubir},
  booktitle = {ICASSP 2018},
  year = {2018}
}