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Novel approach to nonlinear/non-Gaussian Bayesian state estimation

Neil Gordon; David Salmond; A. F. M. Smith · IEE Proceedings F Radar and Signal Processing · 1993

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Author / responsible party
Neil Gordon; David Salmond; A. F. M. Smith
Publisher
IEE Proceedings F Radar and Signal Processing
Year
1993
Language
English

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An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise: it may be applied to any state transition or measurement model. A simulation example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter.

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APA 7

Gordon, N, Salmond, D, & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. https://doi.org/10.1049/ip-f-2.1993.0015

MLA

Gordon, Neil, et al. "Novel approach to nonlinear/non-Gaussian Bayesian state estimation." 1993. https://doi.org/10.1049/ip-f-2.1993.0015.

Chicago

Gordon, Neil, David Salmond, and A. F. M. Smith. 1993. "Novel approach to nonlinear/non-Gaussian Bayesian state estimation.". https://doi.org/10.1049/ip-f-2.1993.0015.

Harvard

Gordon, N, Salmond, D. and Smith, A. F. M. 1993, Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings F Radar and Signal Processing, available at: https://doi.org/10.1049/ip-f-2.1993.0015 [Accessed 8 Aug. 2026].

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Title
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Author / contributors
Neil Gordon; David Salmond; A. F. M. Smith
Publisher
IEE Proceedings F Radar and Signal Processing
Publication year
1993
Language
English

Subjects

Explore related resources through these subjects.

Kalman filter; Gaussian; State vector; Algorithm; Filter (signal processing); Extended Kalman filter; Bayesian probability; Recursive Bayesian estimation; Computer science; Ensemble Kalman filter; Nonlinear system; Noise (video)
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