Publications in Scientific Journals:

T. Li, F. Hlawatsch:
"A Distributed Particle-PHD Filter Using Arithmetic-Average Fusion of Gaussian Mixture Parameters";
Information Fusion, 73 (2021), 111 - 124.

English 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.

Distributed multitarget tracking, distributed PHD filter, arithmetic average fusion, average consensus, flooding, probability hypothesis density, random finite set, Gaussian mixture, sequential Monte Carlo, importance sampling

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.