Resilient Distributed Estimation: Sensor Attacks

TL;DR

This paper introduces the $ ext{SIU}$ algorithm for resilient distributed estimation that guarantees convergence to the true parameter despite sensor attacks affecting less than half of the agents, regardless of network topology.

Contribution

The paper proposes the $ ext{SIU}$ algorithm, a novel method ensuring resilient distributed estimation under sensor attacks with convergence guarantees independent of network topology.

Findings

$ ext{SIU}$ converges at a polynomial rate to the true parameter.

Resilience holds as long as less than half of sensors are attacked.

Performance demonstrated through numerical examples.

Abstract

This paper studies multi-agent distributed estimation under sensor attacks. Individual agents make sensor measurements of an unknown parameter belonging to a compact set, and, at every time step, a fraction of the agents' sensor measurements may fall under attack and take arbitrary values. We present the Saturated Innovation Update ($\mathcal{SIU}$) algorithm for distributed estimation resilient to sensor attacks. Under the iterative $\mathcal{SIU}$ algorithm, if less than one half of the agent sensors fall under attack, then, all of the agents' estimates converge at a polynomial rate (with respect to the number of iterations) to the true parameter. The resilience of $\mathcal{SIU}$ to sensor attacks does not depend on the topology of the inter-agent communication network, as long as it remains connected. We demonstrate the performance of $\mathcal{SIU}$ with numerical examples.