Gaussian process-based Bayesian non-linear filtering for online target tracking

This study deals with the problem of online target tracking based on Gaussian process regression and deterministic sampling. A general mathematical formulation of the non-linear system identification problem is constructed based on the Gaussian process model, in which the computational efficiency is...

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Veröffentlicht in:IET radar, sonar & navigation Jg. 14; H. 3; S. 448 - 458
Hauptverfasser: Lu, Kelin, Sun, Changyin, Zhu, Qian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 01.03.2020
Schlagworte:
Bayes methods
Bayesian nonlinear filtering problem
filtering theory
Gaussian process model
Gaussian processes
Gaussian process‐based Bayesian nonlinear
Gaussian process‐based nonlinear filtering method
general mathematical formulation
learned surrogate model
learning (artificial intelligence)
measurement models
Monte Carlo methods
nonlinear system identification problem
nonlinear transformation method
online case
online target tracking
reentry target tracking scenario
regression analysis
regular Gaussian process regression algorithm
Research Article
stochastic processes
target tracking
unknown dynamics
unknown dynamics
filtering theory
online target tracking
regression analysis
learned surrogate model
nonlinear transformation method
Gaussian process-based Bayesian nonlinear
measurement models
Monte Carlo methods
general mathematical formulation
Bayesian nonlinear filtering problem
Gaussian processes
target tracking
online case
Gaussian process-based nonlinear filtering method
regular Gaussian process regression algorithm
nonlinear system identification problem
Bayes methods
Gaussian process model
stochastic processes
learning (artificial intelligence)
reentry target tracking scenario
ISSN:1751-8784, 1751-8792
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Beschreibung
Zusammenfassung:This study deals with the problem of online target tracking based on Gaussian process regression and deterministic sampling. A general mathematical formulation of the non-linear system identification problem is constructed based on the Gaussian process model, in which the computational efficiency is improved with a sparsification method, and the regular Gaussian process regression algorithm is extended to the online case where the learned surrogate model is able to be updated recursively. After that, the problem of non-linear transformation with stochastic uncertainty is considered, in which the mean and covariance of a random variable is transformed based on a Gaussian process model. Then the proposed non-linear transformation method is applied to solve the Bayesian non-linear filtering problem with unknown dynamics and measurement models. In the end, extensive Monte-Carlo simulations are conducted to verify the performance of the Gaussian process-based non-linear filtering method in a reentry target tracking scenario.
ISSN:1751-8784
1751-8792
DOI:10.1049/iet-rsn.2019.0495