Abstract
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.
Keywords
Kalman filterGaussianState vectorAlgorithmFilter (signal processing)Extended Kalman filterBayesian probabilityRecursive Bayesian estimationComputer scienceEnsemble Kalman filterNonlinear systemNoise (video)State (computer science)Invariant extended Kalman filterNonlinear filterSet (abstract data type)Tracking (education)Gaussian noiseControl theory (sociology)MathematicsArtificial intelligenceFilter design
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Publication Info
- Year
- 1993
- Type
- article
- Volume
- 140
- Issue
- 2
- Pages
- 107-107
- Citations
- 7488
- Access
- Closed
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Cite This
Neil Gordon,
David Salmond,
A. F. M. Smith
(1993).
Novel approach to nonlinear/non-Gaussian Bayesian state estimation.
IEE Proceedings F Radar and Signal Processing
, 140
(2)
, 107-107.
https://doi.org/10.1049/ip-f-2.1993.0015
Identifiers
- DOI
- 10.1049/ip-f-2.1993.0015