Multi-Source IMM-Mixture Track Fusion Using a Tree Structure

Data fusion techniques combine information about an object that is observed by multiple sources. While the fusion of raw measurements provides the best results, the radio link is often limited and restricts the sources to transmitting condensed track data. In this paper, we consider the case that the sources utilize Interacting Multiple Model filters to track the object and the radio link is capable of the transmission of the mixture data. We treat this topic from an application perspective and assume a large number of sources and a fusion center without knowledge about internal filter parameterization. Our aim is to fuse all Interacting Multiple Model modes of all sources for a preferably high estimation quality. To manage the potentially exponential number of mixture components, we perform a modewise fusion and propose a tree structure that enables the reuse of previously processed data. We compare this approach with a central measurement fusion and a moment-matched fusion employing a Monte-Carlo simulation to evaluate the estimation quality. Especially during changes in the motion behavior of the object, a moderate improvement in the estimation quality can be observed when compared to the moment-matched fusion.