Robust Sequential Detection in Distributed Sensor Networks

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.

arXiv comment13 pages, 5 figures

References in corpus (3)

• Old Bands, New Tracks---Revisiting the Band Model for Robust Hypothesis Testing
• Order-2 Asymptotic Optimality of the Fully Distributed Sequential Hypothesis Test
• Optimal sequential multiple hypothesis tests

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• Low-complexity Distributed Detection with One-bit Memory Under Neyman-Pearson Criterion
• An Asymptotically Equivalent GLRT Test for Distributed Detection in Wireless Sensor Networks
• Deep Actor-Critic Reinforcement Learning for Anomaly Detection
• Asymptotically Minimax Robust Hypothesis Testing