NDLI: Adaptive Gauss–Hermite filter for non-linear systems with unknown measurement noise covariance

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Adaptive Gauss–Hermite filter for non-linear systems with unknown measurement noise covariance

Content Provider	IET Digital Library
Author	Dey, Aritro Sadhu, Smita Ghoshal, Tapan Kumar
Abstract	A non-linear adaptive state estimator based on the Gauss–Hermite (GH) quadrature rule has been proposed to suit non-linear signal models where the measurement noise covariance remains unknown. The proposed algorithm which may be used for both parameter and state estimation incorporates online adaptation of the measurement noise covariance ( R ) following maximum-likelihood estimation-based method. The GH quadrature approach has been considered so that the proposed filter may inherit the enhanced estimation accuracy as exhibited by its non-adaptive counterpart. The proposed adaptation algorithm, in contrast to some other reported methods, automatically ensures positive definiteness of the adapted measurement noise covariance. The efficacy of the adaptive algorithm over the non-adaptive GH filter has been demonstrated using Monte Carlo simulation and two case studies. Performance comparison has also been carried out with respect to adaptive unscented Kalman filter with the help of same case studies.
Starting Page	1007
Ending Page	1015
Page Count	9
ISSN	17518822
Volume Number	9
e-ISSN	17518830
Issue Number	Issue 8, Nov (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-smt/9/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2015.0020
Journal	IET Science, Measurement & Technology
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptation Algorithm Adaptive Algorithm Adaptive Estimation Adaptive Filter Adaptive Gauss–Hermite Filter Covariance Analysis Filtering Method in Signal Processing Gauss–Hermite Quadrature Rule Maximum Likelihood Estimation Maximum Likelihood Estimation-based Method Measurement Error Measurement Noise Covariance Monte Carlo Method Monte Carlo Simulation Nonadaptive GH Filter Nonlinear Adaptive State Estimator Nonlinear Estimation Nonlinear Filter Nonlinear Signal Model Nonlinear System Parameter Estimation Signal Processing Theory
Content Type	Text
Resource Type	Article
Subject	Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering

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