A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 1 Issue 2
Apr.  2014

IEEE/CAA Journal of Automatica Sinica

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Haiyang Yu, Yisha Liu and Wei Wang, "Distributed Sparse Signal Estimation in Sensor Networks Using H∞-Consensus Filtering," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 2, pp. 149-154, 2014.
Citation: Haiyang Yu, Yisha Liu and Wei Wang, "Distributed Sparse Signal Estimation in Sensor Networks Using H∞-Consensus Filtering," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 2, pp. 149-154, 2014.

Distributed Sparse Signal Estimation in Sensor Networks Using H-Consensus Filtering


This work was supported by National Natural Science Foundation of China (61305128).

  • This paper is concerned with the sparse signal recovery problem in sensor networks, and the main purpose is to design a filter for each sensor node to estimate a sparse signal sequence using the measurements distributed over the whole network. A so-called l1-regularized H filter is established at first by introducing a pseudo-measurement equation, and the necessary and sufficient condition for existence of this filter is derived by means of Krein space Kalman filtering. By embedding a high-pass consensus filter into l1-regularized H filter in information form, a distributed filtering algorithm is developed, which ensures that all node filters can reach a consensus on the estimates of sparse signals asymptotically and satisfy the prescribed H performance constraint. Finally, a numerical example is provided to demonstrate effectiveness and applicability of the proposed method.


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