Median-Based Resilient Multi-Object Fusion With Application to LMB Densities

This paper deals with multi-object fusion in the presence of misbehaving sensor nodes, 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 labeled multi-Bernoulli densities is also discussed. Finally, the performance of the proposed approach is assessed via simulation experiments on centralized and decentralized multi-target tracking case studies.