Arithmetic Average Density Fusion -- Part III: Heterogeneous Unlabeled and Labeled RFS Filter Fusion

TL;DR

This paper introduces a scalable multisensor multitarget tracking method that fuses heterogeneous random finite set (RFS) filters, such as PHD and multi-Bernoulli filters, using Gaussian mixture consensus to improve detection and localization accuracy.

Contribution

It presents a novel Gaussian mixture-based fusion approach that efficiently combines diverse RFS filters by revising only the weights of local Gaussian components for better robustness.

Findings

Effective fusion of heterogeneous RFS filters demonstrated in simulations

Improved target detection and localization accuracy

Computationally efficient coordinate descent method for weight revision

Abstract

This paper proposes a heterogenous density fusion approach to scalable multisensor multitarget tracking where the inter-connected sensors run different types of random finite set (RFS) filters according to their respective capacity and need. These diverse RFS filters result in heterogenous multitarget densities that are to be fused with each other in a proper means for more robust and accurate detection and localization of the targets. Our approach is based on Gaussian mixture implementations where the local Gaussian components (L-GCs) are revised for PHD consensus, i.e., the corresponding unlabeled probability hypothesis densities (PHDs) of each filter best fit their average regardless of the specific type of the local densities. To this end, a computationally efficient, coordinate descent approach is proposed which only revises the weights of the L-GCs, keeping the other parameters unchanged. In particular, the PHD filter, the unlabeled and labeled multi-Bernoulli (MB/LMB) filters are considered. Simulations have demonstrated the effectiveness of the proposed approach for both homogeneous and heterogenous fusion of the PHD-MB-LMB filters in different configurations.