This paper proposes a distributed method for cooperative target tracking inhierarchical wireless sensor networks. The concept of leader-based information processingis conducted to achieve object positioning, considering a cluster-based network topology.Random timers and local information are applied to adaptively select a sub-cluster for thelocalization task. The proposed energy-efficient tracking algorithm allows each sub-clustermember to locally estimate the target position with a Bayesian filtering framework and aneural networking model, and further performs estimation fusion in the leader node withthe covariance intersection algorithm. This paper evaluates the merits and trade-offs ofthe protocol design towards developing more efficient and practical algorithms for objectposition estimation.
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