Robust Sequential Detection in Distributed Sensor Networks
Information Theory
2018-10-17 v1 math.IT
Abstract
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 (CISPRT). Furthermore, we introduce two different concepts for robustifying the CISPRT and propose four different algorithms, namely, the Least-Favorable-Density-CISPRT, the Median-CISPRT, the M-CISPRT, and the Myriad-CISPRT. 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.
Cite
@article{arxiv.1802.00263,
title = {Robust Sequential Detection in Distributed Sensor Networks},
author = {Mark R. Leonard and Abdelhak M. Zoubir},
journal= {arXiv preprint arXiv:1802.00263},
year = {2018}
}
Comments
13 pages, 5 figures