Distributed recursive least-squares for consensus-based in-network adaptive estimation

Gonzalo Mateos, Ioannis D. Schizas, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

111 Scopus citations


Recursive least-squares (RLS) schemes are of paramount importance for reducing complexity and memory requirements in estimating stationary signals as well as for tracking nonstationary processes, especially when the state and/or data model are not available and fast convergence rates are at a premium. To this end, a fully distributed (D-) RLS algorithm is developed for use by wireless sensor networks (WSNs) whereby sensors exchange messages with one-hop neighbors to consent on the network-wide estimates adaptively. The WSNs considered here do not necessarily possess a Hamiltonian cycle, while the inter-sensor links are challenged by communication noise. The novel algorithm is obtained after judiciously reformulating the exponentially-weighted least-squares cost into a separable form, which is then optimized via the alternating-direction method of multipliers. If powerful error control codes are utilized and communication noise is not an issue, D-RLS is modified to reduce communication overhead when compared to existing noise-unaware alternatives. Numerical simulations demonstrate that D-RLS can outperform existing approaches in terms of estimation performance and noise resilience, while it has the potential of performing efficient tracking.

Original languageEnglish (US)
Pages (from-to)4583-4588
Number of pages6
JournalIEEE Transactions on Signal Processing
Issue number11
StatePublished - Nov 4 2009


  • Distributed estimation
  • RLS algorithm
  • Wireless sensor networks (WSNs)

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