The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Particle Filtering
Abstract
Recent developments have demonstrated that particle filtering is an emerging and powerful methodology, using Monte Carlo methods, for sequential signal processing with a wide range of applications in science and engineering. It has captured the attention of many researchers in various communities, including those of signal processing, statistics and econometrics. Based on the concept of sequential importance sampling and the use of Bayesian theory, particle filtering is particularly useful in dealing with difficult nonlinear and non-Gaussian problems. The underlying principle of the methodology is the approximation of relevant distributions with random measures composed of particles (samples from the space of the unknowns) and their associated weights. First, we present a brief review of particle filtering theory; and then we show how it can be used for resolving many problems in wireless communications. We demonstrate its application to blind equalization, blind detection over flat fading channels, multiuser detection, and estimation and detection of space-time codes in fading channels.
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Cites
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,502
Fundamentals of statistical signal processing: Estimation theory
Prasad, K. V. (1994) · Control Engineering Practice · doi:10.1016/0967-0661(94)90195-3 · Cited by 7,902
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
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(2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,366
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On sequential Monte Carlo sampling methods for Bayesian filtering
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Monte Carlo Strategies in Scientific Computing
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Cited by
Application of data fusion techniques and technologies for wearable health monitoring
King, R. C., Villeneuve, E., White, R. J., Sherratt, R. S., Holderbaum, W., & Harwin, W. S. (2017) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2016.12.011 · Cited by 182
Metadata
- Authors
- Djuric, P. M., Kotecha, J. H., Zhang, J., Huang, Y., Ghirmai, T., Bugallo, M. F., & Miguez, J.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2003-09
- Volume
- 20
- Issue
- 5
- Pages
- 19-38
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W4255133955
- Cited by
- 965
- References
- 26
- FWCI
- 20.11
- Type
- journal article
- Language
- en
- Open access
- closed
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- Djuric, P. M., Kotecha, J. H., Zhang, J., Huang, Y., Ghirmai, T., Bugallo, M. F., & Miguez, J. (2003). Particle Filtering. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2003.1236770
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