Hierarchical particle filtering for target tracking in multi-modal sensor networks

We propose a filtering method, called hierarchical particle filtering, for multi-modal sensor networks in which the unknown state vector is observed, through the measurements, in a hierarchical fashion. We partition the state space and the measurement space into lower dimensional subspaces. At each stage, we find an estimate of one partition using the measurements from the corresponding partition, and the information from the previous stages. We use hierarchical particle filtering for joint initiation, termination and tracking of multiple targets using multi-modal measurements. Numerical simulations demonstrate that the proposed filtering method accurately identifies the number and the categories of targets, and produces a lower mean-squared error (MSE) compared to the MSE obtained using a standard particle filter.