Robust Sequential Detection in Distributed Sensor Networks
Mark R. Leonard, Abdelhak M. Zoubir · IEEE Transactions on Signal Processing · 2018
We consider the problem of sequential binary hypothesis testing with a distributed sensor network in a non-Gaussian noise environment. To this end, we present a general formulation of the Consensus + Innovations Sequential Probability Ratio Test ($\mathcal {CI}$SPRT). Furthermore, we introduce two different concepts for robustifying the$\mathcal {CI}$SPRT and propose four different algorithms, namely the Least-Favorable-Density-$\mathcal {CI}$SPRT, the Median-$\mathcal {CI}$SPRT, the M-$\mathcal {CI}$SPRT, and the Myriad-$\mathcal {CI}$SPRT. Subsequently, we analyze their suitability for different binary hypothesis tests before verifying and evaluating their performance in a shift-in-mean and a shift-in-variance scenario for different network connectivities and amounts of noise contamination.