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Distributed Kalman-Consensus Filtering for Sparse Signal Estimation

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  • Yisha Liu
  • Haiyang Yu
  • Jian Wang

Abstract

A Kalman filtering-based distributed algorithm is proposed to deal with the sparse signal estimation problem. The pseudomeasurement-embedded Kalman filter is rebuilt in the information form, and an improved parameter selection approach is discussed. By introducing the pseudomeasurement technology into Kalman-consensus filter, a distributed estimation algorithm is developed to fuse the measurements from different nodes in the network, such that all filters can reach a consensus on the estimate of sparse signals. Some numerical examples are provided to demonstrate the effectiveness of the proposed approach.

Suggested Citation

  • Yisha Liu & Haiyang Yu & Jian Wang, 2014. "Distributed Kalman-Consensus Filtering for Sparse Signal Estimation," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-7, March.
    • Handle: RePEc:hin:jnlmpe:138146
    DOI: 10.1155/2014/138146
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