  Multi-agent, collaborative sensor fusion is a vital component of a
multi-national intelligence toolkit. In safety-critical and/or contested
environments, adversaries may infiltrate and compromise a number of agents. We
analyze state of the art multi-target tracking algorithms under this
compromised agent threat model. We prove that the track existence probability
test ("track score") is significantly vulnerable to even small numbers of
adversaries. To add security awareness, we design a trust estimation framework
using hierarchical Bayesian updating. Our framework builds beliefs of trust on
tracks and agents by mapping sensor measurements to trust pseudomeasurements
(PSMs) and incorporating prior trust beliefs in a Bayesian context. In case
studies, our trust estimation algorithm accurately estimates the
trustworthiness of tracks/agents, subject to observability limitations.
