A Distributed Particle-PHD Filter with Arithmetic-Average PHD Fusion

Abstract

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.

13 pages, codes available upon e-mail request

References in corpus (2)

• Second Order Statistics Analysis and Comparison between Arithmetic and Geometric Average Fusion
• Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion

Cited by in corpus (9)

• Second Order Statistics Analysis and Comparison between Arithmetic and Geometric Average Fusion
• Fusion of Probability Density Functions
• Arithmetic Average Density Fusion -- Part II: Unified Derivation for Unlabeled and Labeled RFS Fusion
• Arithmetic Average Density Fusion -- Part I: Some Statistic and Information-theoretic Results
• Multi-sensor Suboptimal Fusion Student's $t$ Filter
• Arithmetic Average Density Fusion -- Part III: Heterogeneous Unlabeled and Labeled RFS Filter Fusion
• Arithmetic Average Density Fusion -- Part IV: Distributed Heterogeneous Fusion of RFS and LRFS Filters via Variational Approximation
• Principled information fusion for multi-view multi-agent surveillance systems
• Distributed Gaussian Mixture PHD Filtering under Communication Constraints