Sensor selection for tracking multiple groups of targets
Group target tracking is a challenge for sensor networks. It occurs where large numbers of closely spaced targets move together in different groups. In these applications, the sensor selection scheme plays a vital role in extending network lifetime while providing high tracking accuracy. Existing schemes cause an extreme imbalance between energy usages and tracking accuracy. They are capable of tracking only individual groups and without using prior knowledge about the groups. These problems make them impractical for group target tracking. With the aim of balancing the trade-off between lifetime and accuracy, we present a novel Multi-Sensor Group Tracking (MSGT) scheme. MSGT comprises the following steps to accomplish concurrent tracking of multiple groups: (1) Clustering to capture changes in the behavioural properties of groups, such as formation, merging, and splitting; (2) Sensor selection to activate the contributory sensors for the estimated group regions; and (3) Group tracking using the activated sensors. We develop a probabilistic decision-making strategy that triggers the clustering step adaptively with any detected change in group behavioural patterns. The sensor selection step coordinates periodic selection of leader and tracking sensor nodes in a distributed manner. We introduce cost metrics that include sensor?s energy parameters in the selection of active sensors that fully cover the group regions. The tracking step is a Bayesian modelling of the target groups which uses particle filtering algorithm to estimate the group locations. Simulation results show that our scheme achieves substantial improvements over existing approaches in terms of network lifetime and tracking accuracy.
sensor selection group target tracking network lifetime clustering bayesian estimation