  The paper considers the problem of human-scale RF sensing utilizing a network
of resource-constrained MIMO radars with low range-azimuth resolution. The
radars operate in the mmWave band and obtain time-varying 3D point cloud (PC)
information that is sensitive to body movements. They also observe the same
scene from different views and cooperate while sensing the environment using a
sidelink communication channel. Conventional cooperation setups allow the
radars to mutually exchange raw PC information to improve ego sensing. The
paper proposes a federation mechanism where the radars exchange the parameters
of a Bayesian posterior measure of the observed PCs, rather than raw data. The
radars act as distributed parameter servers to reconstruct a global posterior
(i.e., federated posterior) using Bayesian tools. The paper quantifies and
compares the benefits of radar federation with respect to cooperation
mechanisms. Both approaches are validated by experiments with a real-time
demonstration platform. Federation makes minimal use of the sidelink
communication channel (20 {\div} 25 times lower bandwidth use) and is less
sensitive to unresolved targets. On the other hand, cooperation reduces the
mean absolute target estimation error of about 20%.
