Hierarchical particle filtering for multi-modal data fusion with application to multiple-target tracking ?
We considered the problem of recursive Bayesian estimation in hierarchical models that use multi-modal data. We proposed a new filter, called the hierarchical particle filter (HPF) to find an estimate of the unknown state in several stages. At each stage, HPF computes an estimate of the state partition using posterior weights from previous stages as extrinsic information. We used HPF for the joint initiation, termination and tracking of multiple-targets using multi-modal sensors. We used a sensor network comprising three different types of sensors: a radar, an infrared camera and a human scout.
data fusion multi-modal sensors sensor network multitarget tracking independent likelihood sequential bayesian filtering