New Methodologies for the Tracking of Low-Observable Objects

Multiobject tracking refers to the problem of estimating the time dependent number and states of multiple objects from measurements provided by one or multiple sensors. Applications include surveillance, autonomous driving, indoor localization, oceanography, robotics, and biomedical analytics. The sensor measurements are either preprocessed to reduce data flow and computational complexity, which leads to an approach known as detect-then-track (DTT) multiobject tracking, or unprocessed, which leads to an approach known as track-before-detect (TBD). A particularly challenging task in multiobject tracking is the tracking of low-observable objects. Low- observable objects give rise to sensor measurements of high uncertainty. Possible causes are limited sensing and detection capabilities of the sensors or objects that maneuver in areas of reduced observability, such as in underwater environments. The goal of the proposed postdoctoral stay is to devise high-performing yet efficient methodologies and algorithms for the tracking of multiple low- observable objects. In the proposed research, we focus on the TBD paradigm, which is especially suited for the tracking of low-observable objects because it does not discard relevant sensor information. In particular, for the problem modeling and the corresponding estimation process, we combine the techniques of random finite sets and belief propagation, which is expected to lead to powerful yet low-computational tracking algorithms. Additionally, we contribute to DTT multiobject tracking using multiple sensors. We will address distributed multisensor scenarios, i.e., without a central processing unit, where each sensor is only able to communicate with its neighbors. Most state-of-the-art algorithms fuse probability distributions using a hard association of tracked objects. To avoid an incorrect association, which often occurs in the case of low-observable objects, we propose to use a soft (probabilistic) association of objects.