Robust Sequential Testing of Multiple Hypotheses in Distributed Sensor Networks
Mark R. Leonard, Maximilian Stiefel, Michael Fauß, Abdelhak M. Zoubir · 2018
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.