Decentralized set-membership adaptive estimation for clustered sensor networks

This paper proposes a clustering approach to parameter estimation in distributed sensor networks. The proposed approach is an alternative to the conventional centralized and decentralized approaches. This is made possible by the unique adaptive estimation architecture, U-SHAPE, stemming from set-membership adaptive filtering. At the expense of a slightly degraded mean-square error performance (comparing to the least-squares approach), the proposed approach offers improved data processing flexibility in a distributed sensor network, reduced signal processing hardware and reduced communication bandwidth and power requirements.