Efficient use of the network's resources to collect information about objects (events) in a given volume of interest (VOI) is a key challenge in large-scale sensor networks. Multi-sensor multi-target tracking in s...
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Efficient use of the network's resources to collect information about objects (events) in a given volume of interest (VOI) is a key challenge in large-scale sensor networks. Multi-sensor multi-target tracking in surveillance applications is an example where the network's success in tracking targets, efficiently and effectively, hinges significantly on the network's ability to allocate the right set of sensors to the right set of targets so as to achieve optimal performance which minimizes the number of uncovered targets. This task can be even more complicated when both the sensors and the targets are mobile. To ensure timely tracking of mobile targets, the surveillance sensor network needs to perform the following tasks in real-time: (i) target-to-sensor allocation;(ii) sensor mobility control and coordination. The computational complexity of these two tasks presents a challenge, particularly in large scale dynamic network applications. This paper proposes a formulation based on the semi-flocking algorithm and the distributed constraint optimization problem (DCOP). The semi-flocking algorithm performs multi-target motion control and coordination, a DCOP modeling algorithm performs the target engagement task. As will be demonstrated experimentally in the paper, this algorithmic combination provides an effective approach to the multisensor/multi-target engagement problem, delivering optimal target coverage as well as maximum sensors utilization.
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