NDLI: Comparison of distributed receding horizon filtering for linear discrete-time systems with uncertainties

Content Provider	IEEE Xplore Digital Library
Author	Ju-hong Yoon Seung-hwan Bae Shin, V.
Copyright Year	2010
Description	Author affiliation: Dept. of Mechatronics, Gwanju Institute of Science and Technology, Gwangju, Republic of Korea (Ju-hong Yoon; Seung-hwan Bae; Shin, V.)
Abstract	A distributed receding horizon filtering for linear discrete-time systems with uncertainties is presented. The choice of receding horizon strategy makes the estimation fusion algorithms robust against dynamic model uncertainties. All distributed fusion algorithms are based on the fusion formulas which represent weighted sums of local receding horizon Kalman estimates with matrix weights. The fusion weights are calculated by using four algorithms: convex combination, optimal fusion, covariance intersection, and median fusion. The comparison results of the fusion algorithms in terms of estimation accuracy and computation cost are discussed.
Starting Page	117
Ending Page	122
File Size	372454
Page Count	6
File Format	PDF
ISBN	9781424455850
e-ISBN	9781424455867
DOI	10.1109/ICCAE.2010.5451410
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-02-26
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Mechatronics Filtering Kalman filter Nonlinear filters distributed filtering Time measurement Electronic mail Noise measurement Finite impulse response filter Robustness Kalman filters Multisensory system receding horizon strategy
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

An Optimal Receding Horizon FIR Filter for Continuous-Time Linear Systems

Distributed fusion receding horizon filtering

Distributed receding horizon filtering in discrete-time dynamic systems

A design of optimal receding horizon filter for linear systems with uncertainty

Comparison of Distributed Fusion Filters for Linear Dynamic System with Uncertainty

A receding horizon Kalman filter with the estimated initial state on the horizon

Receding horizon estimators for systems with delayed state and measurement

A Receding Horizon Kalman FIR Filter for Linear Continuous-Time Systems (1999)

A Robust FIR Filter for Linear Discrete-Time State–Space Signal Models With Uncertainties

Comparison of distributed receding horizon filtering for linear discrete-time systems with uncertainties

Similar Documents

An Optimal Receding Horizon FIR Filter for Continuous-Time Linear Systems

Distributed fusion receding horizon filtering

Distributed receding horizon filtering in discrete-time dynamic systems

A design of optimal receding horizon filter for linear systems with uncertainty

Comparison of Distributed Fusion Filters for Linear Dynamic System with Uncertainty

A receding horizon Kalman filter with the estimated initial state on the horizon

Receding horizon estimators for systems with delayed state and measurement

A Receding Horizon Kalman FIR Filter for Linear Continuous-Time Systems (1999)

A Robust FIR Filter for Linear Discrete-Time State–Space Signal Models With Uncertainties

Comparison of distributed receding horizon filtering for linear discrete-time systems with uncertainties