A distributed particle-PHD filter using arithmetic-average fusion of Gaussian mixture parameters

Abstract

We propose a particle-based distributed PHD filter for tracking the states of 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 resulting distributed PHD filtering framework is able to integrate both particle-based and GM-based local PHD filters. Simulations demonstrate the excellent performance and small communication and computation requirements of our filter.