The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Distributed Kalman filtering based on consensus strategies
Open accessAbstract
In this paper, we consider the problem of estimating the state of a dynamical system from distributed noisy measurements. Each agent constructs a local estimate based on its own measurements and on the estimates from its neighbors. Estimation is performed via a two stage strategy, the first being a Kalman-like measurement update which does not require communication, and the second being an estimate fusion using a consensus matrix. In particular we study the interaction between the consensus matrix, the number of messages exchanged per sampling time, and the Kalman gain for scalar systems. We prove that optimizing the consensus matrix for fastest convergence and using the centralized optimal gain is not necessarily the optimal strategy if the number of exchanged messages per sampling time is small. Moreover, we show that although the joint optimization of the consensus matrix and the Kalman gain is in general a non-convex problem, it is possible to compute them under some relevant scenarios. We also provide some numerical examples to clarify some of the analytical results and compare them with alternative estimation strategies.
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
Consensus Problems in Networks of Agents With Switching Topology and Time-Delays
Olfati-Saber, R., & Murray, R. M. (2004) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2004.834113 · Cited by 12,788
Doob, J. L., & Doob, J. L. (1953) · CERN Document Server (European Organization for Nuclear Research) · Cited by 9,053
Gantmacher, F. R. (1984) · UCLA - Biblioteca de Ciencias y Tecnologia Felix Morales Bueno · Cited by 8,520
Coordination of groups of mobile autonomous agents using nearest neighbor rules
Jadbabaie, A., Lin, J., & Morse, A. S. (2003) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2003.812781 · Cited by 8,339
Consensus seeking in multiagent systems under dynamically changing interaction topologies
Ren, W., & Beard, R. W. (2005) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2005.846556 · Cited by 6,569
Parallel and Distributed Computation: Numerical Methods
Bertsekas, D. P., & Tsitsiklis, J. N. (1989) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 5,293
Fast linear iterations for distributed averaging
Xiao, L., & Boyd, S. (2004) · Systems & Control Letters · doi:10.1016/j.sysconle.2004.02.022 · Cited by 2,706
Stability of multiagent systems with time-dependent communication links
Moreau, L. (2005) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2004.841888 · Cited by 2,706
Distributed asynchronous deterministic and stochastic gradient optimization algorithms
Tsitsiklis, J., Bertsekas, D., & Athans, M. (1986) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1986.1104412 · Cited by 1,967
A 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
Cited by
Kalman-Consensus Filter : Optimality, stability, and performance
Olfati-Saber, R. (2009) · Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference · doi:10.1109/cdc.2009.5399678 · Cited by 677
Sayed, A. H. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2306253 · Cited by 587
Distributing the Kalman Filter for Large-Scale Systems
Khan, U. A., & Moura, J. M. F. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2008.927480 · Cited by 481
Chen, J., Cao, X., Cheng, P., Xiao, Y., & Sun, Y. (2010) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2010.2043038 · Cited by 295
Distributed particle filtering in agent networks: A survey, classification, and comparison
Hlinka, O., Hlawatsch, F., & Djuric, P. M. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2219652 · Cited by 259
Metadata
- Authors
- Carli, R., Chiuso, A., Schenato, L., & Zampieri, S.
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 2008-05
- Volume
- 26
- Issue
- 4
- Pages
- 622-633
- ISSN
- 0733-8716 (ISSN-L)
- OpenAlex
- W2098865243
- Cited by
- 511
- References
- 41
- FWCI
- 42.25
- 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: Distributed Kalman filtering based on consensus strategies. https://app.cochat.ai/papers/10.1109/jsac.2008.080505
- The paper
- Carli, R., Chiuso, A., Schenato, L., & Zampieri, S. (2008). Distributed Kalman filtering based on consensus strategies. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/jsac.2008.080505
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.
