NDLI: Sensor scheduling based on permissible consecutive observation loss

Content Provider	IEEE Xplore Digital Library
Author	Chen Wang Zhiyun Lin Gangfeng Yan
Copyright Year	2012
Description	Author affiliation: Asus Intell. Syst. Lab., Zhejiang Univ., Hangzhou, China (Chen Wang; Zhiyun Lin; Gangfeng Yan)
Abstract	In this paper, we investigate the problem of scheduling multiple Kalman filters for multiple processes, where each process is observed by a Kalman filter and at each time step only one Kalman filter could obtain observation. The objective is to find a scheduling scheme such that the desired estimation qualities for all the processes could be satisfied. Two novel concepts, permissible consecutive observation loss (PCOL) and least consecutive observation (LCO), are introduced to characterize feasible observation sequences. Two methods, namely, threshold method and periodic method, are developed to calculate PCOL and LCO for a given desired estimation quality. Based on derived PCOL and LCO requirements, two efficient algorithms are presented to find a feasible observation sequence. Both rigorous analysis and simulation results are provided to validate the approaches.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	5562
Ending Page	5567
File Size	138973
Page Count	6
File Format	PDF
ISBN	9781457710957
ISSN	07431619
e-ISBN	9781457710964
e-ISBN	9781467321020
DOI	10.1109/ACC.2012.6315320
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Kalman filters Covariance matrix Estimation error Vectors Noise Noise measurement
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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

Kalman filtering with optimal sensor motion planning

Kalman Filtering With Scheduled Measurements

Invariant Momentum-tracking Kalman Filter for attitude estimation

Dynamic target navigation based on multisensor Kalman filtering and neighbor discovery algorithm

A distributed estimation method for sensor networks based on Pareto optimization

Optimal Distributed Kalman Filtering Fusion Algorithm Without Invertibility of Estimation Error and Sensor Noise Covariances

Efficient real time OD matrix estimation based on Principal Component Analysis

The multisensor data fusion parallel algorithm based on Kalman filtering

Optimal Kalman filter fusion with singular covariances of filter error

Sensor scheduling based on permissible consecutive observation loss

Similar Documents

Kalman filtering with optimal sensor motion planning

Kalman Filtering With Scheduled Measurements

Invariant Momentum-tracking Kalman Filter for attitude estimation

Dynamic target navigation based on multisensor Kalman filtering and neighbor discovery algorithm

A distributed estimation method for sensor networks based on Pareto optimization

Optimal Distributed Kalman Filtering Fusion Algorithm Without Invertibility of Estimation Error and Sensor Noise Covariances

Efficient real time OD matrix estimation based on Principal Component Analysis

The multisensor data fusion parallel algorithm based on Kalman filtering

Optimal Kalman filter fusion with singular covariances of filter error

Sensor scheduling based on permissible consecutive observation loss