  Collective motion among biological organisms such as insects, fish, and birds
has motivated considerable interest not only in biology but also in distributed
robotic systems. In a robotic or biological swarm, anomalous agents (whether
malfunctioning or nefarious) behave differently than the normal agents and
attempt to hide in the "chaos" of the swarm. By defining a graph structure
between agents in a swarm, we can treat the agents' properties as a graph
signal and use tools from the field of graph signal processing to understand
local and global swarm properties. Here, we leverage this idea to show that
anomalous agents can be effectively detected using their impacts on the graph
Fourier structure of the swarm.
