The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Kalman-Consensus Filter : Optimality, stability, and performance
Abstract
One of the fundamental problems in sensor networks is to estimate and track the state of targets (or dynamic processes) of interest that evolve in the sensing field. Kalman filtering has been an effective algorithm for tracking dynamic processes for over four decades. Distributed Kalman Filtering (DKF) involves design of the information processing algorithm of a network of estimator agents with a two-fold objective: (1) estimate the state of the target of interest and (2) reach a consensus with neighboring estimator agents on the state estimate. We refer to this DKF algorithm as Kalman-Consensus Filter (KCF). The main contributions of this paper are as follows: (i) finding the optimal decentralized Kalman-Consensus filter and showing that its computational and communication costs are not scalable in n and (ii) introducing a scalable suboptimal Kalman-Consensus Filter and providing a formal stability and performance analysis of this distributed and cooperative filtering algorithm. Kalman-Consensus Filtering algorithm is applicable to sensor networks with variable topology including mobile sensor networks and networks with packet-loss.
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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 · cited 1×
“The Kalman-Consensus (KC) estimator consists of two terms: a standard Kalman estimator and a consensus term on prior estimates similar to the ones appearing in the averageconsensus algorithm [17].”
MethodSelf-citationConsensus and Cooperation in Networked Multi-Agent Systems
Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2006.887293 · Cited by 10,522 · cited 1×
“From consensus theory [16] (and algebraic graph theory [9] for the scalar case), we know that the following quadratic property holds for the m-dimensional Laplacian L̂ = Im⊗L of any undirected graph G:”
BackgroundSelf-citationGodsil, C., & Royle, G. (2001) · Graduate Texts in Mathematics · doi:10.1007/978-1-4613-0163-9 · Cited by 8,154
Flocking for Multi-Agent Dynamic Systems: Algorithms and Theory
Olfati-Saber, R. (2006) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2005.864190 · Cited by 5,037
Self-citationintroduction to random signals and applied kalman filtering
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Coverage Control for Mobile Sensing Networks
Cortes, J., Martinez, S., Karatas, T., & Bullo, F. (2004) · IEEE Transactions on Robotics and Automation · doi:10.1109/tra.2004.824698 · Cited by 2,710
Distributed Kalman filtering for sensor networks
Olfati-Saber, R. (2007) · 2007 46th IEEE Conference on Decision and Control · doi:10.1109/cdc.2007.4434303 · Cited by 1,586
Self-citationNericell: rich monitoring of road and traffic conditions using mobile smartphones
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Cited by
Sayed, A. H. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2306253 · Cited by 587
Sayed, 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
Diffusion Adaptation Over Networks
Sayed, A. H. (2014) · Academic Press Library in Signal Processing · doi:10.1016/b978-0-12-411597-2.00009-6 · Cited by 465
Xia, X., Hashemi, E., Xiong, L., & Khajepour, A. (2023) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2022.3174511 · Cited by 200
Metadata
- Authors
- Olfati-Saber, R.
- Venue
- Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference
- Publisher
- IEEE
- Published
- 2009-12
- Pages
- 7036-7042
- OpenAlex
- W2158191982
- Cited by
- 677
- References
- 20
- FWCI
- 12.46
- Type
- conference paper
- Language
- en
- Open access
- closed
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