The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Distributed particle filtering in agent networks: A survey, classification, and comparison
Abstract
Distributed particle filter (DPF) algorithms are sequential state estimation algorithms that are executed by a set of agents. Some or all of the agents perform local particle filtering and interact with other agents to calculate a global state estimate. DPF algorithms are attractive for large-scale, nonlinear, and non-Gaussian distributed estimation problems that often occur in applications involving agent networks (ANs). In this article, we present a survey, classification, and comparison of various DPF approaches and algorithms available to date. Our emphasis is on decentralized ANs that do not include a central processing or control unit.
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Cites
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Consensus and Cooperation in Networked Multi-Agent Systems
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Filtering via Simulation: Auxiliary Particle Filters
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Distributed Kalman filtering for sensor networks
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Metadata
- Authors
- Hlinka, O., Hlawatsch, F., & Djuric, P. M.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2013-01
- Volume
- 30
- Issue
- 1
- Pages
- 61-81
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W2050386482
- Cited by
- 259
- References
- 70
- FWCI
- 25.96
- Type
- journal article
- Language
- en
- Open access
- closed
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