  We propose a particle-based distributed PHD filter for tracking an unknown,
time-varying number of targets. To reduce communication, the local PHD filters
at neighboring sensors communicate Gaussian mixture (GM) parameters. In
contrast to most existing distributed PHD filters, our filter employs an
`arithmetic average' fusion. For particles--GM conversion, we use a method that
avoids particle clustering and enables a significance-based pruning of the GM
components. For GM--particles conversion, we develop an importance sampling
based method that enables a parallelization of filtering and
dissemination/fusion operations. The proposed distributed particle-PHD filter
is able to integrate GM-based local PHD filters. Simulations demonstrate the
excellent performance and small communication and computation requirements of
our filter.
