NDLI: Sequential fusion for asynchronous multi-sensor system based on Kalman filter

Content Provider	IEEE Xplore Digital Library
Author	Ye Junjun Peng Dongliang Ge Quanbo
Copyright Year	2009
Description	Author affiliation: Institute of Information and Control, Hangzhou Dianzi University, 310018, China (Ye Junjun; Peng Dongliang; Ge Quanbo)
Abstract	This paper proposes a novel sequential asynchronous fusion algorithm by using the idea of sequential discretization of the sampling points based on a continuous and distributed multi-sensor linear dynamic system. Firstly, it maps and unifies all measurements in the reference frame and clock with fusion centre. Secondly, selecting every sampling time in the fusion period to discretize the continuous state system sequentially, we get the state equation and the relevant measurement equation between every sampling point in this period. Finally, using the best linear Kalman filter in the sense of LMMSE directly, the sequential filtering fusion of asynchronous sampling measurements in this period can be realized. Compared with the existing typical asynchronous algorithms which depend on equivalent pseudo-measurements, the proposed algorithm has lower computational load, better real-time and accurateness. This paper elaborates the form of this new algorithm, and finally computer simulation demonstrates validity of the new algorithms.
Starting Page	4677
Ending Page	4681
File Size	343604
Page Count	5
File Format	PDF
ISBN	9781424427222
DOI	10.1109/CCDC.2009.5191805
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering Asynchronous sampling system Computer simulation Nonlinear filters Control systems Time measurement Electronic mail Equations Sequential Discretization Sampling methods State estimation Clocks
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

Performance monitoring of a CSTR plant using asynchronous data fusion based on Extended Kalman Filter

Comparison of Distributed Fusion Filters for Linear Dynamic System with Uncertainty

Multi-sensor information fusion extended Kalman particle filter

Distributed fusion filter for time-delay systems with colored measurement noise

Sequential fusion and state estimation for asynchronous multirate multisensor dynamic systems

Design of information fusion filter for a class of multi-sensor asynchronous sampling systems

Measurement Fusion Kalman Filters for Descriptor Stochastic Systems

Simultaneous spatiotemporal bias and state estimation for asynchronous multi-sensor system

A New Estimation Method for Multisensor Fusion by using Interval Analysis and Particle Filtering

Sequential fusion for asynchronous multi-sensor system based on Kalman filter

Similar Documents

Performance monitoring of a CSTR plant using asynchronous data fusion based on Extended Kalman Filter

Comparison of Distributed Fusion Filters for Linear Dynamic System with Uncertainty

Multi-sensor information fusion extended Kalman particle filter

Distributed fusion filter for time-delay systems with colored measurement noise

Sequential fusion and state estimation for asynchronous multirate multisensor dynamic systems

Design of information fusion filter for a class of multi-sensor asynchronous sampling systems

Measurement Fusion Kalman Filters for Descriptor Stochastic Systems

Simultaneous spatiotemporal bias and state estimation for asynchronous multi-sensor system

A New Estimation Method for Multisensor Fusion by using Interval Analysis and Particle Filtering

Sequential fusion for asynchronous multi-sensor system based on Kalman filter