RKHS Based State Estimator for Radar Sensor in Indoor Application

Kumar Singh, Uday; Shankar, Bhavani; Alaee, Mohammad

Reference : RKHS Based State Estimator for Radar Sensor in Indoor Application


	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/49495

Title :	RKHS Based State Estimator for Radar Sensor in Indoor Application
Language :	English
Author, co-author :	Kumar Singh, Uday [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SPARC >]
	Shankar, Bhavani [> > > Assistant professor]
	Alaee, Mohammad [> > > Research Scientist]
Publication date :	23-Apr-2022
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	Radar Conference 2022
Event date :	from 21-03-2022 to 25-03-2022
Abstract :	[en] For the estimation of targets’ states (location, velocity, and acceleration) from nonlinear radar measurements, usually, the improved version of well known Kalman filter: extended Kalman filter (EKF) and unscented Kalman filter (UKF) are used. However, EKF and UKF approximates the nonlinear measurement function either by Jacobian or using sigma points. Consequently, because of the approximation of the measurement function, the EKF and UKF cannot achieve high estimation accuracy. The potential solution is to replace the approximation of nonlinear measurement function with its estimate, obtained in high dimensional reproducing kernel Hilbert space (RKHS). An ample amount of research has been done in this direction, and the combined filter is termed RKHS based Kalman filter. However, there is a shortage of literature dealing with estimating the dynamic state of the target in an indoor environment using RKHS based Kalman filter. Therefore, in this paper, we propose the use of RKHS based Kalman filter for indoor application. Specifically, we validate the suitability of the RKHS based Kalman filtering approach using simulations performed over three different target motion models.
Funders :	Luxembourg National Research Fund
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/49495
FnR project :	FnR ; FNR12734677 > Bjorn Ottersten > SPRINGER > Signal Processing For Next Generation Radar > 01/09/2019 > 31/08/2022 > 2018

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