  Formation control (FC) of multi-agent plays a critical role in a wide variety
of fields. In the absence of absolute positioning, agents in FC systems rely on
relative position measurements with respect to their neighbors. In distributed
filter design literature, relative observation models are comparatively
unexplored, and in FC literature, uncertainty models are rarely considered. In
this article, we aim to bridge the gap between these domains, by exploring
distributed filters tailored for relative FC of swarms. We propose
statistically robust data models for tracking relative positions of agents in a
FC network, and subsequently propose optimal Kalman filters for both
centralized and distributed scenarios. Our simulations highlight the benefits
of these estimators, and we identify future research directions based on our
proposed framework.
