Multi-Object Association decision algorithms with belief functions

Multi-Object Association (MOA) consists in determining, at each processing cycle, the best associations set linking the detected objects to the already known ones. The aim is then to determine if the objects are propagated, appearing or disappearing. This association is relevant when the data imperfections (imprecision, inaccuracy, etc.) are considered. As the Transferable Belief Model (TBM) helps to consider these imperfections, it represents an interesting framework for MOA. The focus is here placed on TBM-based MOA decision-making, i.e. the selection of the most relevant associations among the possible ones. In this context, the comparison of existing decision algorithms is provided. Based on the analysis of their performance, two decision approaches are proposed. Simulations, performed considering a literature example, show the differences between the algorithms and the interests of the proposed solutions.