The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Diffusion recursive least-squares for distributed estimation over adaptive networks
Open accessAbstract
We study the problem of distributed estimation over adaptive networks where a collection of nodes are required to estimate in a collaborative manner some parameter of interest from their measurements. The centralized solution to the problem uses a fusion center, thus, requiring a large amount of energy for communication. Incremental strategies that obtain the global solution have been proposed, but they require the definition of a cycle through the network. We propose a diffusion recursive least-squares algorithm where nodes need to communicate only with their closest neighbors. The algorithm has no topology constraints, and requires no transmission or inversion of matrices, therefore saving in communications and complexity. We show that the algorithm is stable and analyze its performance comparing it to the centralized global solution. We also show how to select the combination weights optimally.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Haykin, S. (1986) · Cited by 12,603
(1991) · Springer eBooks · doi:10.1007/978-3-7091-2840-4 · Cited by 5,095
Bellanger, M. (1987) · Signal Processing · doi:10.1016/0165-1684(87)90147-2 · Cited by 2,961
Alexander, S. T. (1986) · Springer eBooks · doi:10.1007/978-1-4612-4978-8 · Cited by 2,646
Fundamentals of Adaptive Filtering
Ramirez, P. S. (2002) · The Kluwer International Series in Engineering and Computer Science · doi:10.1007/978-1-4757-3637-3_2 · Cited by 1,958
Fundamentals of adaptive filtering
Sayed, A. H. (2003) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1,673
Self-citationA scheme for robust distributed sensor fusion based on average consensus
Xiao, L., Boyd, S., & Lall, S. (2005) · doi:10.5555/1147685.1147698 · Cited by 1,170
Instrumenting the world with wireless sensor networks
Estrin, D., Girod, L., Pottie, G., & Srivastava, M. (2002) · 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221) · doi:10.1109/icassp.2001.940390 · Cited by 1,164
Consensus Filters for Sensor Networks and Distributed Sensor Fusion
Olfati-Saber, R., & Shamma, J. S. (2006) · Proceedings of the 44th IEEE Conference on Decision and Control · doi:10.1109/cdc.2005.1583238 · Cited by 982
Cited by
Sayed, A. H. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2306253 · Cited by 587
Self-citationSayed, A. H., Tu, S.-Y., Chen, J., Zhao, X., & Towfic, Z. J. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2231991 · Cited by 494
Self-citation
Metadata
- Authors
- Cattivelli, F. S., Lopes, C. G., & Sayed, A. H.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2008-05
- Volume
- 56
- Issue
- 5
- Pages
- 1865-1877
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2119434824
- Cited by
- 623
- References
- 20
- FWCI
- 27.31
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Diffusion recursive least-squares for distributed estimation over adaptive networks. https://app.cochat.ai/papers/10.1109/tsp.2007.913164
- The paper
- Cattivelli, F. S., Lopes, C. G., & Sayed, A. H. (2008). Diffusion recursive least-squares for distributed estimation over adaptive networks. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2007.913164
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
