  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.
