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.
Bibliographical noteFunding Information:
Manuscript received January 07, 2009; accepted May 01, 2009. First published June 02, 2009; current version published October 14, 2009. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Biao Chen. Work in this paper was supported by the NSF Grants CCF 0830480 and CON 014658; and also by USDoD ARO Grant No. W911NF-05-1-0283, as well as through collaborative participation in the C&N Consortium sponsored by the U. S. ARL under the CTA Program, Cooperative Agreement DAAD19-01-2-0011. The U. S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon.
- Distributed estimation
- RLS algorithm
- Wireless sensor networks (WSNs)