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Gaussian Variationa...
Gaussian Variational State Estimation for Nonlinear State-Space Models
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- Courts, Jarrad (författare)
- Univ Newcastle, Fac Engn & Built Environm, Callaghan, NSW 2308, Australia.
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- Wills, Adrian (författare)
- Univ Newcastle, Fac Engn & Built Environm, Callaghan, NSW 2308, Australia.
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- Schön, Thomas B., Professor, 1977- (författare)
- Uppsala universitet,Avdelningen för systemteknik,Artificiell intelligens
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Univ Newcastle, Fac Engn & Built Environm, Callaghan, NSW 2308, Australia Avdelningen för systemteknik (creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2021
- 2021
- Engelska.
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 69, s. 5979-5993
- Relaterad länk:
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https://urn.kb.se/re...
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visa fler...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- In this paper, the problem of state estimation, in the context of both filtering and smoothing, for nonlinear state-space models is considered. Due to the nonlinear nature of the models, the state estimation problem is generally intractable as it involves integrals of general nonlinear functions and the filtered and smoothed state distributions lack closed-form solutions. As such, it is common to approximate the state estimation problem. In this paper, we develop an assumed Gaussian solution based on variational inference, which offers the key advantage of a flexible, but principled, mechanism for approximating the required distributions. Our main contribution lies in a new formulation of the state estimation problem as an optimisation problem, which can then be solved using standard optimisation routines that employ exact first- and second-order derivatives. The resulting state estimation approach involves a minimal number of assumptions and applies directly to nonlinear systems with both Gaussian and non-Gaussian probabilistic models. The performance of our approach is demonstrated on several examples; a challenging scalar system, a model of a simple robotic system, and a target tracking problem using a von Mises-Fisher distribution and outperforms alternative assumed Gaussian approaches to state estimation.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- Smoothing methods
- State estimation
- Optimization
- Filtering
- Kalman filters
- Standards
- Time measurement
- Assumed density
- non-Gaussian noise
- nonlinear filtering
- smoothing
- variational inference
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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