Hierarchical fusion in particle filtering track-before-detect

Track fusion is the problem of combining tracks based on different sensor observations. In the sequential Monte Carlo framework, track fusion is solved by either imposing linear or Gaussian assumptions, or relying on kernel density estimation (KDE). In this paper, we introduce a novel track fusion algorithm suited to the hierarchical multi-sensor architecture. The algorithm can be incorporated in the particle filtering framework without restricting the densities by imposing assumptions, or requiring the non-trivial selection of additional parameters, as e.g., is needed in KDE. Furthermore, the proposed method is equivalent to the optimal centralised fusion architecture, in which all sensor measurements are communicated to the fusion node. Numerical results show that the newly proposed method outperforms the existing methods either by reducing estimation errors or by reducing the computation time significantly.