Skip to main content
Article thumbnail
Location of Repository

Robust set-theoretic distributed detection in diffusion networks

By R.L.G. Cavalcante and S. Stanczak


We propose novel set-theoretic distributed adaptive filters for cooperative signal detection in diffusion networks, a problem that has been gaining attention owing to its application to cooperative cognitive radio networks. In the proposed method, nodes in a network detect the presence of a signal of interest by means of an inner product between the current term of a series and a known reference vector. Each term of the series is computed from information fusion among neighboring nodes and projections onto closed convex sets, which are constructed with a priori knowledge of the signal of interest and measurements obtained by nodes. In particular, we show that sets based on a priori knowledge are useful to decrease the communication overhead and to provide good detection performance. Our results are rigorous in the sense that no approximations are used to prove convergence properties. In particular, we show conditions to guarantee that the series converge to a point that can reliably identify the signal of interest. Furthermore, we also show that recent results in distributed optimization for dynamic systems can be used to derive algorithms where nodes exchange not only the current vectors of their sequences (as in previous distributed set-theoretic filters), but also side information that influences the above-mentioned sets

Year: 2012
DOI identifier: 10.1109/ICASSP.2012.6288734
OAI identifier:
Provided by: Fraunhofer-ePrints
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.