A Distributed Particle-PHD Filter with Arithmetic-Average PHD Fusion
For multi-target tracking in sensor networks, this work offers a practical distributed filter that reduces communication overhead while maintaining accuracy.
The paper proposes a distributed particle-PHD filter using arithmetic-average fusion for tracking an unknown number of targets, achieving excellent performance with low communication and computation costs.
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.