NDLI: Least-squares support vector machine-based Kalman filtering for GNSS navigation with dynamic model real-time correction

Content Provider	IET Digital Library
Author	Zhou, Zebo Li, Yong Fu, Chen Rizos, Chris
Abstract	The accuracy of Kalman filtering (KF) relies on the quality of the observations as well as the dynamic model. However, the dynamic model is usually assumed to be invariant, which is not realistic in real global navigation satellite system (GNSS) navigation applications due to unexpected vehicular motion. A new algorithm is proposed to enhance the KF by using the least-squares support vector machine (LSSVM) technique. The LSSVM-enhanced KF (LSSVM-KF) adaptively estimates the dynamic modelling bias from historical information, and then uses the bias estimate to compensate the dynamic model. The algorithm treats the dynamic model bias as a time-variant ambiguous function which is trained with the LSSVM. A k-fold cross-validation method is developed to tune the training parameters of the LSSVM. With the corrected dynamic model, the KF is implemented to estimate the navigation parameters. To integrate LSSVM with KF, the unscented transformation is introduced to numerically compute the covariance of the LSSVM training. To verify the algorithm, simulation, semi-simulation and real GNSS vehicular experiments were carried out. The results show that the LSSVM-KF can adequately adapt to time-variant dynamics and achieve a reliable and accurate GNSS navigation solution.
Starting Page	528
Ending Page	538
Page Count	11
ISSN	17518784
Volume Number	11
e-ISSN	17518792
Issue Number	Issue 3, Mar (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/11/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2016.0422
Journal	IET Radar, Sonar & Navigation
Publisher Date	2016-10-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Estimation CKF Communication Computing Conventional Kalman Filtering Dynamic Model Real-time Correction Dynamic Modelling Bias Filtering Method in Signal Processing GNSS Navigation GNSS Vehicular Experiments Interpolation And Function Approximation K-fold Cross-validation Method Kalman Filter Knowledge Engineering Technique Least Squares Approximation Least-squares Support Vector Machine LSSVM Technique LSSVM Training Covariance LSSVM-enhanced KF LSSVM-KF Neural Network-based KF Numerical Analysis Radio Navigation And Direction Finding Sage-Husa Adaptive KF Satellite Navigation Support Vector Machine Telecommunication Computing Time-variant Dynamics Unexpected Vehicular Motion Unscented Transformation
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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