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A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Abstract
Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.
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Cites
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Cited by
Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560
Li, R., Pei, S., Chen, B., Song, Y., Zhang, T., Yang, W., & Shaman, J. (2020) · Science · doi:10.1126/science.abb3221 · Cited by 3,633
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952
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He, S., & Chan, S.-H. G. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2464084 · Cited by 1,355
Monocular Pedestrian Detection: Survey and Experiments
Enzweiler, M., & Gavrila, D. M. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.260 · Cited by 1,213
Metadata
- Authors
- Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2002
- Volume
- 50
- Issue
- 2
- Pages
- 174-188
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2160337655
- Cited by
- 11,502
- References
- 2
- FWCI
- 154.57
- Type
- journal article
- Language
- en
- Open access
- closed
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- Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing. https://doi.org/10.1109/78.978374
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