In this paper, a novel consensus-based adaptive algorithm for distributed target tracking in large scale camera\nnetworks is presented, aimed at situations characterized by limited sensing range, high-level clutter, and possibly\nocculted targets. The concept of Integrated Probabilistic Data Association (IPDA) is introduced in the distributed\nadaptive tracker design so that the proposed algorithm, named IPDA Adaptive Consensus Filter (IPDA-ACF),\nincorporates probabilities of acquiring target-originated measurements, conditioned on either target perceivability or\ntarget existence. A distributed adaptation scheme represents the core element of the algorithm, allowing fast\nconvergence under a large variety of operating conditions, emphasizing the influence of the nodes with the highest\nprobability of obtaining target-originated measurements. A theoretical analysis of stability and reduction of noise\ninfluence allows getting an insight into the relationship between the local trackers and the global consensus scheme.\nA comparison with analogous existing methods done by extensive simulations shows that the proposed method\nachieves the best performance, in spite of lower communication and computation requirements.
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