Abstract
The Poisson multi-Bernoulli mixture (PMBM) filter is extended for
distributed implementation using a wireless sensor network. At the core
of the proposed networking approach, the PMBM posterior is decomposed
into two parts corresponding to the undetected and detected targets,
respectively. Fusion is motivated to be performed with regard to the
latter only which is represented by MBM based on a distributed flooding
algorithm for internode communication, which iteratively shares the MBMs
between neighbor sensors. Then, a suboptimal “best-fit-ofmixture”
principle is followed at each local sensor to find a MBM that best fits
the mixture of MBMs aggregated from distinct sensors, leading to an
arithmetic average (AA) of these MBMs. We prove the exact closure of the
MBM-AA fusion and discuss its sub-optimality and limitations. Simulation
demonstrates the effectiveness and limitations of our approach.