Mobile Multi-Target Tracking in Two-Tier Hierarchical Wireless Sensor Networks

We propose a two-tier hierarchical Wireless Sensor Network (WSN) to address the Mobile Multi-Target Tracking (MMTT) problem. We develop the Cluster Head (CH) and cluster member election schemes using a Particle Cardinality Balanced Multi-Bernoulli (CBMeMBer) Filtering algorithm. Under our proposed schemes, the CHs with better views of the tracked targets are activated at each time step and fuse their estimations sequentially. Within the cluster of each activated CH, the sensors gaining more information on the tracked targets transmit their measurement-sets to the CH. The CH processes the information locally and estimates the number of targets and their states. Applying the CBMeMBer Filtering technique, we also develop the data fusion method to determine the order of CHs during the sequential data fusion and the number of active sensors within the cluster of each activated CH. The simulation results show that our proposed scheduling schemes and data fusion methods perform well even when the target dynamics and/or measurement process are severely nonlinear.