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Volume 1 Issue 2
Apr.  2014

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2014  >  1(2): 149-154
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.
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Distributed Sparse Signal Estimation in Sensor Networks Using H∞-Consensus Filtering

  • 1. Research Center of Information and Control, Dalian University of Technology, Dalian 116024, China;

  • 2. School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China;

  • 3. Research Center of Information and Control, Dalian University of Technology, Dalian 116024, China

Funds:

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

    Abstract
  • 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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