More Web Proxy on the site http://driver.im/

research-article

Random weighting estimation for fusion of multi-dimensional position data

Authors:

Bijan ShirinzadehAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 180, Issue 24

Pages 4999 - 5007

https://doi.org/10.1016/j.ins.2010.08.023

Published: 01 December 2010 Publication History

Abstract

This paper adopts the concept of random weighting estimation to multi-sensor data fusion. It presents a new random weighting estimation methodology for optimal fusion of multi-dimensional position data. A multi-sensor observation model is constructed for multi-dimensional position. Based on this observation model, a random weighting estimation algorithm is developed for estimation of position data from single sensors. Using the random weighting estimations from each single sensor, an optimization theory is established for optimal fusion of multi-sensor position data. Experimental results demonstrate that the proposed methodology can effectively fuse multi-sensor dimensional position data, and the fusion accuracy is much higher than that of the Kalman fusion method.

References

[1]

N.A. Carlson, Federated square root filter for decentralized parallel processes, IEEE Transactions on Aerospace and Electronic Systems, 26 (1990) 517-525.

[2]

Z.-L. Deng, Y. Gao, L. Mao, Y. Li, G. Hao, New approach to information fusion steady-state Kalman filtering, Automatica, 41 (2005) 1695-1707.

Digital Library

[3]

D. Fox, J. Hightower, L. Liao, D. Schulz, G. Borriello, Bayesian filtering for location estimation, IEEE Pervasive Computing Magazine, 2 (2003) 24-33.

Digital Library

[4]

S. Gao, Z. Feng, Y. Zhong, B. Shirinzadeh, Random weighting estimation of parameters in generalized Gaussian distribution, Information Sciences, 178 (2008) 2275-2281.

Digital Library

[5]

S. Gao, Z. Zhang, B. Yang, The random weighting estimate of quantile process, Information Sciences, 164 (2004) 139-146.

Digital Library

[6]

S. Gao, J. Zhang, T. Zhou, Law of large number for sample mean of random weighting estimate, Information Sciences, 155 (2003) 151-156.

Digital Library

[7]

S. Gao, Y. Zhong, Random weighting estimation of kernel density, Journal of Statistical Planning and Inference, 140 (2010) 2403-2407.

[8]

S. Gao, Y. Zhong, X. Zhang, B. Shirinzadeh, Multi-sensor data fusion for INS/GPS/SAR integrated navigation system, Aerospace Science and Technology, 13 (2009) 232-237.

[9]

Q. Guo, S. Chen, H. Leung, S. Liu, Covariance intersection based image fusion technique with application to pansharpening in remote sensing, Information Sciences, 180 (2010) 3434-3443.

Digital Library

[10]

R. Kumar, M. Wolenetz, B. Agarwalla, A framework for distributed data fusion, Information Fusion, 8 (2007) 227-251.

[11]

J. Ma, J. Zhang, J. Yang, N. Zhang, An improved information fusion algorithm based SVM, in: IEEE International Conference on Computational Intelligence and Security Workshops, Harbin, China, 2007, pp. 397-400.

[12]

A. Makarenko, H. Durrant-Whyte, Decentralized Bayesian algorithms for active sensor networks, Information Fusion, 7 (2006) 418-433.

Digital Library

[13]

L.I. Perlovsky, Cognitive high level information fusion, Information Sciences, 177 (2007) 2099-2118.

Digital Library

[14]

A.L. Ralescu, D.A. Ralescu, Y. Yamakata, Inference by aggregation of evidence with applications to fuzzy probabilities, Information Sciences, 177 (2007) 378-387.

[15]

S.-L. Sun, Multi-sensor optimal fusion fixed-interval Kalman smoothers, Information Fusion, 9 (2008) 293-299.

Digital Library

[16]

S.-L. Sun, Z.-L. Deng, Multi-sensor optimal information fusion Kalman filter, Automatica, 40 (2004) 1017-1023.

Digital Library

[17]

R.R. Yager, A framework for multi-source data fusion, Information Sciences, 163 (2004) 175-200.

Digital Library

[18]

R.R. Yager, A framework for reasoning with soft information, Information Sciences, 180 (2010) 1390-1406.

Digital Library

[19]

G. Yang, Y. Lin, P. Bhattacharya, A driver fatigue recognition model based on information fusion and dynamic Bayesian network, Information Sciences, 180 (2010) 1942-1954.

Digital Library

[20]

Z. Zheng, Random weighting method, Acta Mathematicae Applicatae Sinica, 10 (1987) 247-253.

Cited By

Gao YGao YMao Y(2019)A Study on Moving Window Adaptively Weighting Estimation Method2019 IEEE International Conference on Mechatronics and Automation (ICMA)10.1109/ICMA.2019.8816279(198-202)Online publication date: 4-Aug-2019
https://dl.acm.org/doi/10.1109/ICMA.2019.8816279
Zhu HLeung HHe Z(2013)A variational Bayesian approach to robust sensor fusion based on Student-t distributionInformation Sciences: an International Journal10.1016/j.ins.2012.09.017221(201-214)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1016/j.ins.2012.09.017

Recommendations

An Algorithm of Data Fusion Using Artificial Neural Network and Dempster-Shafer Evidence Theory
CASE '09: Proceedings of the 2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009)

A new algorithm of data fusion using neural networks and Dempster-Shafer (D-S) evidence theory is presented in this paper to overcome these faults of data fusion, i.e., low accurate identification, bad stabilization and solution of uncertainty in some ...
Multi-source data fusion study in scientometrics

This paper provides an introduction to multi-source data fusion (MSDF) and comprehensively overviews the ingredients and challenges of MSDF in scientometrics. As compared to the MSDF methods in the sensor and other fields, and considering the features ...
Multi sensor data fusion with filtering
AIC'05: Proceedings of the 5th WSEAS International Conference on Applied Informatics and Communications

The purpose of data fusion is to produce an improved model or estimate of a system from a set of independent data sources. There are various multisensor data fusion approaches, of which Kalman filtering is one of the most significant. Methods for Kalman ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 180, Issue 24

December, 2010

422 pages

ISSN:0020-0255

Issue’s Table of Contents

Copyright © Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 December 2010

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Gao YGao YMao Y(2019)A Study on Moving Window Adaptively Weighting Estimation Method2019 IEEE International Conference on Mechatronics and Automation (ICMA)10.1109/ICMA.2019.8816279(198-202)Online publication date: 4-Aug-2019
https://dl.acm.org/doi/10.1109/ICMA.2019.8816279
Zhu HLeung HHe Z(2013)A variational Bayesian approach to robust sensor fusion based on Student-t distributionInformation Sciences: an International Journal10.1016/j.ins.2012.09.017221(201-214)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1016/j.ins.2012.09.017

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents