Distributed object tracking based on information weighted uav selection with priory objects

In this work, we propose a distributed cubature information filter based multi-object tracking method with an information weighted selection for unmanned aerial vehicle (UAV) networks. In an UAV network, multiple UAVs can observe multiple objects in the region of interest. Further, the UAVs can exchange the objects local information among themselves and fuse them together to obtain the global state of the objects. As the number of UAVs in the network increases, the information exchange among the UAVs suffers from scalability, bandwidth and energy limitations. Thus, it is usually desirable to allow only a desired number of UAVs with highly relevant information to participate in the information exchange. In our approach, the innovation vector within the information filtering framework is used to calculate the amount of information associated with each UAV. Further, a threshold based selection mechanism is proposed to facilitate the UAVs to take independent decisions on whether to participate in the information exchange or not. In the proposed method, the UAVs take the decision to participate in the information exchange based on the information associated with a dynamic subset of objects known as priory objects while keeping the total number of information exchanges in the network to a desired number (on average).