Attack Resilient Distributed Estimation: A Consensus Plus Innovations Approach

This paper studies fully distributed parameter estimation under sensor attacks. A group of agents make measurements of a bounded parameter and a fraction of the agents' sensor measurements falls under integrity attack. When a sensor is under attack, its measurement can take any value as determined by the attacker. The agents exchange messages over a communication network to ensure that all agents are able to correctly estimate the parameter of interest. This paper presents a consensus+innovations algorithm for attack resilient distributed parameter estimation. The algorithm achieves the same level of resilience as the most resilient centralized estimator if less than half of the agents' sensors are under attack, then all agents correctly estimate the parameter of interest, regardless of the topology of the inter-agent communication network, as long as it is connected. This paper illustrates the performance of the algorithm under adversarial attack through numerical simulations.