The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Distributed Kalman filtering for sensor networks
Abstract
In this paper, we introduce three novel distributed Kalman filtering (DKF) algorithms for sensor networks. The first algorithm is a modification of a previous DKF algorithm presented by the author in CDC-ECC '05. The previous algorithm was only applicable to sensors with identical observation matrices which meant the process had to be observable by every sensor. The modified DKF algorithm uses two identical consensus filters for fusion of the sensor data and covariance information and is applicable to sensor networks with different observation matrices. This enables the sensor network to act as a collective observer for the processes occurring in an environment. Then, we introduce a continuous-time distributed Kalman filter that uses local aggregation of the sensor data but attempts to reach a consensus on estimates with other nodes in the network. This peer-to-peer distributed estimation method gives rise to two iterative distributed Kalman filtering algorithms with different consensus strategies on estimates. Communication complexity and packet-loss issues are discussed. The performance and effectiveness of these distributed Kalman filtering algorithms are compared and demonstrated on a target tracking task.
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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
Self-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×
“Control-theoretic consensus algorithms have proven to be effective tools for performing network-wide distributed computation tasks such as computing aggregate quantities and functions over networks [13], [12], [17], [8], [23], [24], [21], [5].”
BackgroundSelf-citationAn algorithm for tracking multiple targets
Reid, D. (1979) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1979.1102177 · Cited by 3,059
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
Cited by
Sayed, A. H. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2306253 · Cited by 587
Shen, B., Wang, Z., & Hung, Y. S. (2010) · Automatica · doi:10.1016/j.automatica.2010.06.025 · Cited by 490
Distributed Consensus on Robot Networks for Dynamically Merging Feature-Based Maps
Aragues, R., Cortes, J., & Sagues, C. (2012) · IEEE Transactions on Robotics · doi:10.1109/tro.2012.2192012 · Cited by 119
Metadata
- Authors
- Olfati-Saber, R.
- Venue
- 2007 46th IEEE Conference on Decision and Control
- Publisher
- IEEE
- Published
- 2007
- Pages
- 5492-5498
- ISSN
- 0191-2216 (ISSN-L)
- OpenAlex
- W2153344006
- Cited by
- 1,586
- References
- 4
- FWCI
- 28.62
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Olfati-Saber, R. (2007). Distributed Kalman filtering for sensor networks. 2007 46th IEEE Conference on Decision and Control. https://doi.org/10.1109/cdc.2007.4434303
Data sources
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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