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.
|Title of host publication||Proceedings of the 19th International Conference on Information Fusion (ISIF 2016)|
|Place of Publication||United States|
|Number of pages||7|
|Publication status||Published - 5 Aug 2016|
|Event||19th International Conference on Information Fusion, FUSION 2016 - Heidelberg, Germany, Heidelberg, Germany|
Duration: 5 Jul 2016 → 8 Jul 2016
Conference number: 19
|Conference||19th International Conference on Information Fusion, FUSION 2016|
|Abbreviated title||FUSION 2016|
|Period||5/07/16 → 8/07/16|
|Other||5-8 July 2016|
- Multi-sensor fusion
- particle filtering
Iglesias Garcia, F., Mandal, P. K., Bocquel, M., & Driessen, H. (2016). Hierarchical fusion in particle filtering track-before-detect. In Proceedings of the 19th International Conference on Information Fusion (ISIF 2016) (pp. 743-749). United States: IEEE.