Median-based resilient multi-object fusion with application to LMB densities.pdf (710.65 kB)
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Median-based resilient multi-object fusion with application to LMB densities

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posted on 2023-03-26, 03:45 authored by yao zhou, Giorgio Battistelli, Luigi Chisci, Lin GaoLin Gao, gaiyou li, ping wei

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


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University of Electronic Science and Technology of China

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  • China