Lee, Deok-Jin (2004-05). Nonlinear bayesian filtering with applications to estimation and navigation. Doctoral Dissertation. Thesis uri icon

abstract

  • In principle, general approaches to optimal nonlinear filtering can be described in a unified way from the recursive Bayesian approach. The central idea to this recur- sive Bayesian estimation is to determine the probability density function of the state vector of the nonlinear systems conditioned on the available measurements. However, the optimal exact solution to this Bayesian filtering problem is intractable since it requires an infinite dimensional process. For practical nonlinear filtering applications approximate solutions are required. Recently efficient and accurate approximate non- linear filters as alternatives to the extended Kalman filter are proposed for recursive nonlinear estimation of the states and parameters of dynamical systems. First, as sampling-based nonlinear filters, the sigma point filters, the unscented Kalman fil- ter and the divided difference filter are investigated. Secondly, a direct numerical nonlinear filter is introduced where the state conditional probability density is calcu- lated by applying fast numerical solvers to the Fokker-Planck equation in continuous- discrete system models. As simulation-based nonlinear filters, a universally effective algorithm, called the sequential Monte Carlo filter, that recursively utilizes a set of weighted samples to approximate the distributions of the state variables or param- eters, is investigated for dealing with nonlinear and non-Gaussian systems. Recentparticle filtering algorithms, which are developed independently in various engineer- ing fields, are investigated in a unified way. Furthermore, a new type of particle filter is proposed by integrating the divided difference filter with a particle filtering framework, leading to the divided difference particle filter. Sub-optimality of the ap- proximate nonlinear filters due to unknown system uncertainties can be compensated by using an adaptive filtering method that estimates both the state and system error statistics. For accurate identification of the time-varying parameters of dynamic sys- tems, new adaptive nonlinear filters that integrate the presented nonlinear filtering algorithms with noise estimation algorithms are derived. For qualitative and quantitative performance analysis among the proposed non- linear filters, systematic methods for measuring the nonlinearities, biasness, and op- timality of the proposed nonlinear filters are introduced. The proposed nonlinear optimal and sub-optimal filtering algorithms with applications to spacecraft orbit es- timation and autonomous navigation are investigated. Simulation results indicate that the advantages of the proposed nonlinear filters make these attractive alterna- tives to the extended Kalman filter.
  • In principle, general approaches to optimal nonlinear filtering can be described
    in a unified way from the recursive Bayesian approach. The central idea to this recur-
    sive Bayesian estimation is to determine the probability density function of the state
    vector of the nonlinear systems conditioned on the available measurements. However,
    the optimal exact solution to this Bayesian filtering problem is intractable since it
    requires an infinite dimensional process. For practical nonlinear filtering applications
    approximate solutions are required. Recently efficient and accurate approximate non-
    linear filters as alternatives to the extended Kalman filter are proposed for recursive
    nonlinear estimation of the states and parameters of dynamical systems. First, as
    sampling-based nonlinear filters, the sigma point filters, the unscented Kalman fil-
    ter and the divided difference filter are investigated. Secondly, a direct numerical
    nonlinear filter is introduced where the state conditional probability density is calcu-
    lated by applying fast numerical solvers to the Fokker-Planck equation in continuous-
    discrete system models. As simulation-based nonlinear filters, a universally effective
    algorithm, called the sequential Monte Carlo filter, that recursively utilizes a set of
    weighted samples to approximate the distributions of the state variables or param-
    eters, is investigated for dealing with nonlinear and non-Gaussian systems. Recentparticle filtering algorithms, which are developed independently in various engineer-
    ing fields, are investigated in a unified way. Furthermore, a new type of particle
    filter is proposed by integrating the divided difference filter with a particle filtering
    framework, leading to the divided difference particle filter. Sub-optimality of the ap-
    proximate nonlinear filters due to unknown system uncertainties can be compensated
    by using an adaptive filtering method that estimates both the state and system error
    statistics. For accurate identification of the time-varying parameters of dynamic sys-
    tems, new adaptive nonlinear filters that integrate the presented nonlinear filtering
    algorithms with noise estimation algorithms are derived.
    For qualitative and quantitative performance analysis among the proposed non-
    linear filters, systematic methods for measuring the nonlinearities, biasness, and op-
    timality of the proposed nonlinear filters are introduced. The proposed nonlinear
    optimal and sub-optimal filtering algorithms with applications to spacecraft orbit es-
    timation and autonomous navigation are investigated. Simulation results indicate
    that the advantages of the proposed nonlinear filters make these attractive alterna-
    tives to the extended Kalman filter.

publication date

  • May 2004