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

Content Provider	IET Digital Library
Author	Lu, Kelin Sun, Changyin Zhu, Qian
Abstract	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.
Starting Page	448
Ending Page	458
Page Count	11
ISSN	17518784
Volume Number	14
e-ISSN	17518792
Issue Number	Issue 3, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/14/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2019.0495
Journal	IET Radar, Sonar & Navigation
Publisher Date	2020-01-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bayes Method Bayesian Nonlinear Filtering Problem Filtering Method in Signal Processing Filtering Theory Gaussian Process Model Gaussian Process-based Bayesian Nonlinear Gaussian Process-based Nonlinear Filtering Method Gaussian Processes General Mathematical Formulation Interpolation And Function Approximation Knowledge Engineering Technique Learned Surrogate Model Learning in AI Measurement Model Monte Carlo Method Nonlinear System Identification Problem Nonlinear Transformation Method Numerical Analysis Online Case Online Target Tracking Optimisation Technique Probability Theory Reentry Target Tracking Scenario Regression Analysis Regular Gaussian Process Regression Algorithm Signal Processing Theory Statistics Stochastic Linearised SCUC Target Tracking Unknown Dynamics
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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