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

This article, the third part of a series of papers on the arithmetic average density fusion approach and its application for target tracking, proposes the first heterogenous density fusion approach to scalable multisensor multitarget tracking where the interconnected 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 probability hypothesis density (PHD) consensus, i.e., the corresponding unlabeled PHDs of each filter best fit their average regardless of the specific form 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.