Abstract-Accurately estimating the current positions of moving objects is a challenging task due to the various forms of data uncertainty (e.g. limited sensor precision, periodic updates from continuously moving objects). However, in many cases, groups of objects tend to exhibit similarities in their movement behavior. For example, vehicles in a convoy or animals in a herd both exhibit tightly coupled movement behavior within the group. While such statistical dependencies often increase the computational complexity necessary for capturing this additional structure, they also provide useful information which can be utilized to provide more accurate location estimates. In this paper, we propose a novel model for accurately tracking coordinated groups of mobile uncertain objects. We introduce an exact and more efficient approximate inference algorithm for updating the current location of each object upon the arrival of new (uncertain) location observations. Additionally, we derive probability bounds over the groups in order to process probabilistic threshold range queries more efficiently. Our experimental evaluation shows that our proposed model can provide 4X improvements in tracking accuracy over competing models which do not consider group behavior. We also show that our bounds enable us to prune up to 50% of the database, resulting in more efficient processing over a linear scan.
Larusso et al. REF propose a Kalman Filter-based model to update the current location of uncertain mobile objects with a probability bounds of errors.
Efficient tracking and querying for coordinated uncertain mobile objects
2013 IEEE 29th International Conference on Data Engineering (ICDE)
2013 IEEE 29th International Conference on Data Engineering (ICDE)
Computer Science