A decentralized Poisson multi-Bernoulli filter is proposed to track multiple extended targets using multiple sensors. Independent filters estimate the targets presence, state, and shape using a Gaussian process extent model; a decentralized filter is realized through fusion of the filters posterior densities. An efficient implementation is achieved by parametric state representation, utilization of single hypothesis tracks, and fusion of target information based on a fusion mapping. Numerical results demonstrate the performance.
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