Abstract
This paper deals with multi-object fusion in the presence of misbehaving
sensor nodes, for example due to faults or adversarial attacks. In this
setting, the main challenge is to identify and then remove messages
coming from corrupted nodes. To this end, a three-step method is
proposed, where the first step consists of choosing a reference density
among the received ones on the basis of a minimum upper median
divergence criterion. Then, thresholding on the divergence from the
reference density is performed to derive a subset of densities to be
fused. Finally, the remaining densities are fused following either the
generalized covariance intersection (GCI) or minimum information loss
(MIL) criterion. The implementation of the proposed method for resilient
fusion of label multi-Bernoulli densities is also discussed. Finally,
the performance of the proposed approach is assessed via simulation
experiments on a multi-target tracking case study