Resilient Distributed Estimation Through Adversary Detection
Yuan Chen, Soummya Kar, José M. F. Moura · IEEE Transactions on Signal Processing · 2018
This paper studies resilient multiagent distributed estimation of an unknown vector parameter when a subset of the agents is adversarial. We present and analyze a flag raising distributed estimator (FRDE) that allows the agents under attack to perform accurate parameter estimation and detect the adversarial agents. The FRDE algorithm is a consensus+innovations estimator in which agents combine estimates of neighboring agents (consensus) with local sensing information (innovations). We establish that, under FRDE, either the uncompromised agents' estimates are almost surely consistent, or the uncompromised agents detect compromised agents (with arbitrarily small false alarm probability) if and only if the network of uncompromised agents is connected and globally observable. Numerical examples illustrate the performance of FRDE.