Novel approach to nonlinear/non-Gaussian Bayesian state estimation

Novel approach to nonlinear/non-Gaussian Bayesian state estimation

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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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      • , Stochastic processes and filtering theory
    2. 2)
      • Nonlinear Bayesian estimation using Gaussian sum approximation
    3. 3)
      • Approximate non-Gaussian filtering with linear state and observation relations
    4. 4)
      • Dynamic generalised linear models and Bayesian forecasting (with discussion)
    5. 5)
      • Bayes theorem and digital realization for nonlinear filters
    6. 6)
      • Recursive Bayesian estimation using piece-wise constant approximations
    7. 7)
      • Recursive estimation for nonlinear dynamic systems, Bayesian analysis of time series and dynamic models
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      • Non-Gaussian state-space modelling of non-stationary time series (with discussion)
    9. 9)
      • Pole, A., West, M.: `Efficient numerical integration in dynamic models', 136, Research report, 1988
    10. 10)
      • A Monte-Carlo approach to nonnormal and nonlinear state space modelling
    11. 11)
      • Modelling with mixtures (with discussion), Bayesian statistics 4
    12. 12)
      • Monte Carlo integration in general dynamic models
    13. 13)
      • , Density estimation for statistics and data analysis
    14. 14)
      • Bayesian statistics without tears: a sampling-resampling perspective
    15. 15)
      • A Bayesian approach to problems in stochastic estimation and control
    16. 16)
      • Bayesian forecasting (with discussion)
    17. 17)
      • Utilization of modified polar coordinates for bearings-only tracking

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