Robust distributed sequential hypothesis testing for detecting a random signal in non-Gaussian noise

Abstract

This paper addresses the problem of sequential binary hypothesis testing in a multi-agent network to detect a random signal in non-Gaussian noise. To this end, the con-sensus+innovations sequential probability ratio test (ciSPRT) is generalized for arbitrary binary hypothesis tests and a robust version is developed. Simulations are performed to validate the performance of the proposed algorithms in terms of the average run length (ARL) and the error probabilities.