Nonlinear bayesian filtering with applications to estimation and navigation

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dc.contributor.advisor Alfriend , Kyle T . en_US
dc.contributor.committeeMember Junkins , John L . en_US
dc.creator Lee , Deok -Jin en_US 2005 -08 -29T14 :37 :18Z 2014 -02 -19T19 :20 :37Z 2005 -08 -29T14 :37 :18Z 2014 -02 -19T19 :20 :37Z 2004 -05 en_US 2005 -08 -29T14 :37 :18Z
dc.identifier.uri http : / /hdl .handle .net /1969 .1 /2269
dc.description.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 . en_US
dc.format.extent 2072696 bytes
dc.format.medium electronic en_US
dc.format.mimetype application /pdf
dc.language.iso en _US en_US
dc.publisher Texas A &M University en_US
dc.subject statistical nonlinear filtering en_US
dc.title Nonlinear bayesian filtering with applications to estimation and navigation en_US
dc.type Book en
dc.type.genre Electronic Dissertation en_US
dc.type.material text en_US
dc.format.digitalOrigin born digital en_US


Nonlinear bayesian filtering with applications to estimation and navigation. Available electronically from http : / /hdl .handle .net /1969 .1 /2269 .

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