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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Vydáno v:	IET radar, sonar & navigation Ročník 14; číslo 3; s. 448 - 458
Hlavní autoři:	Lu, Kelin, Sun, Changyin, Zhu, Qian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 01.03.2020
Témata:	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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Shrnutí:	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