This paper presents a filter for online data association problems in high-dimensional spaces. The key innovation is a representation of the data association posterior in information form, in which the ``proximity'' of objects and tracks are expressed by a numerical links. Updating these links requires linear time, compared to exponential time required for computing posterior probabilities. The paper derives the algorithm formally, and provides comparative results for using data obtained by real-world camera array and by a large-scale sensor network simulation.
@INPROCEEDINGS{Schumitsch05a,
AUTHOR = {B. Schumitsch and S. Thrun and G. Bradski and K. Olukotun},
TITLE = {The Information-Form Data Association Filter },
YEAR = {2005},
BOOKTITLE = {Proceedings of Conference on Neural Information Processing Systems (NIPS)},
PUBLISHER = {MIT Press},
ADDRESS = {Cambridge, MA}
}