Robust target tracking using distributed unmanned aerial vehicle networks

In this article, a target tracking scheme for operation in distributed unmanned aerial vehicle (UAV) networks in which sensors may read the target position incorrectly is proposed. The proposed scheme operates two algorithms concurrently: semi-decentralized dynamic data fusion and fault detection. The semi-decentralized dynamic data fusion algorithm employs a median-consensus algorithm using extended non-faulty neighbours (whose sensor readings for the target position are within a prescribed tolerance level) and subsequently makes local estimates of the target position converge to nearly the actual target position. Meanwhile, the fault detection algorithm first asks each UAV to find the global median of the local target position through extended neighbours, and then diffuses the determined global median to all the UAVs in the network. As a result, the fault detection algorithm allows UAVs to detect and isolate faulty sensors quickly and to carry on target tracking in the semi-decentralized dynamic data fusion mode. As opposed to existing target tracking schemes, the proposed scheme is deterministic and guarantees the complete detection and isolation of faulty sensors on UAVs and thus successful target tracking. Numerical examples as well as experimental results are provided to support the developed ideas.