Heterogeneous Unlabeled and Labeled RFS Filter Fusion for Scalable Multisensor Multitarget Tracking

This paper proposes a heterogenous density fusion approach to scalable multisensor multitarget tracking where the local, inter-connected sensors run different types of random finite set (RFS) filters according to their respective capacity and need. They 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 recent work has exposed a key common property of effective arithmetic average (AA) fusion approaches to both unlabeled and labeled RFS filters which are all built on averaging their relevant un-labeled/labeled probability hypothesis densities (PHDs). Thanks to this, this paper proposes the first ever heterogenous unlabeled and labeled RFS filter cooperation approach based on Gaussian mixture implementations where the local Gaussian components (L-GCs) are so optimized that the resulting unlabeled PHDs best fit their AA, regardless of the specific type of the local densities. To this end, a computationally efficient, approximate approach is proposed which only revises the weights of the L-GCs, keeping the other parameters of L-GCs 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.