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A Review of Data Fusion Techniques

by Afnan Alofi, Anwaar Alghamdi, Razan Alahmadi, Najla Aljuaid, Hemalatha M.
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 167 - Number 7
Year of Publication: 2017
Authors: Afnan Alofi, Anwaar Alghamdi, Razan Alahmadi, Najla Aljuaid, Hemalatha M.
10.5120/ijca2017914318

Afnan Alofi, Anwaar Alghamdi, Razan Alahmadi, Najla Aljuaid, Hemalatha M. . A Review of Data Fusion Techniques. International Journal of Computer Applications. 167, 7 ( Jun 2017), 37-41. DOI=10.5120/ijca2017914318

@article{ 10.5120/ijca2017914318,
author = { Afnan Alofi, Anwaar Alghamdi, Razan Alahmadi, Najla Aljuaid, Hemalatha M. },
title = { A Review of Data Fusion Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 167 },
number = { 7 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 37-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume167/number7/27786-2017914318/ },
doi = { 10.5120/ijca2017914318 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:14:13.737856+05:30
%A Afnan Alofi
%A Anwaar Alghamdi
%A Razan Alahmadi
%A Najla Aljuaid
%A Hemalatha M.
%T A Review of Data Fusion Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 167
%N 7
%P 37-41
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In many cases, researchers use more than one sensor and synthesize their raw data to generate more meaningful information that can be of greater value than single source data. The process of merging multiple data and knowledge from different sources to represent the object into a regular, accurate, useful, meaningful representation is known as data fusion. This article summarizes the state of data fusion and compares relevant techniques. We explain possible data fusion classifications and review the most common fusion methods such as Kalman filter and The Bayesian Methods. Then we evaluate these methods and discuss the advantages and disadvantages of each method.

References
  1. "Multisensor Fusion Of Target Attributes And Kinematics". Decision And Control Including The Symposium On Adaptive Processes. IEEE, 1981. Print.
  2. "Multisensor Integration And Fusion: Issues And Approaches". Orlando Technical Symposium. 1988. Print.
  3. Elmenreich, Wilfried. An Introduction To Sensor Fusion. Austria: N.p., 2002.
  4. Azimirad, Ehsan, And ,Javad Haddadnia. "A Comprehensive Review Of The Multi-Sensor Data Fusion Architectures". Journal of Theoretical and Applied Information Technology 71.1992-8645 (2015): n. pag. Print.
  5. Xinhan, Huang, and Wang Min. "Multi-Sensor Data Fusion Structures In Autonomous Systems". Lnlernailonal Symposium On Intelligent Control. Houston, Texas: IEEE, 2003. Print.
  6. H. Choset, K. Lynch and S. Hutchinson, Principles of robot motion, 1st ed. Cambridge, Mass.: Bradford, 2005.
  7. "How a Kalman filter works, in pictures | Bzarg", Bzarg.com, 2017. [Online]. Available: http://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/. [Accessed: 19- Apr- 2017].
  8. Faragher, Ramsey. "Understanding The Basis Of The Kalman Filter Via A Simple And Intuitive Derivation". Signal Processing Magazine 2012: n. pag. Print.
  9. Salmond, David, and Neil Gordon. An Introduction To Particle Filters. 2015. Print.
  10. Castanedo, Federico. "A Review Of Data Fusion Techniques". The Scientific World Journal 2013 (2013): 1-19. Web.
  11. Lekkala, Kiran Kumar, and Vinay Kumar Mittal. "Accurate And Augmented Navigation For Quadcopter Based On Multi-Sensor Fusion". 2016 IEEE Annual India Conference (INDICON) (2016): n. pag. Web. 14 Apr. 2017.
  12. LaViola, J.J. "A Comparison Of Unscented And Extended Kalman Filtering For Estimating Quaternion Motion". Proceedings of the 2003 American Control Conference, 2003. n. pag. Web. 14 Apr. 2017.
  13. Rampelli, M., and D. Jena. "Advantage Of Unscented Kalman Filter Over Extended Kalman Filter In Dynamic State Estimation Of Power System Network". Michael Faraday IET International Summit 2015 (2015): n. pag. Web. 14 Apr. 2017.
  14. Zeng, Chan, and Weimin Li. "Application Of Extended Kalman Filter For Tracking High Dynamic GPS Signal". 2016 IEEE International Conference on Signal and Image Processing (ICSIP) (2016): n. pag. Web. 14 Apr. 2017.
  15. Marron, M. et al. "Comparing A Kalman Filter And A Particle Filter In A Multiple Objects Tracking Application". 2007 IEEE International Symposium on Intelligent Signal Processing (2007): n. pag. Web. 14 Apr. 2017.
  16. K. Khoshelhama, C. Nardinocchia and S. Nedkova, "A Comparison Of Bayesian And Evidence-Based Fusion Methods For Automated Building Detection In Aerial Data", The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 7, p. 1184, 2008.
  17. Abdulhafiz, Waleed A., and Alaa Khamis. "Bayesian Approach With Pre- And Post-Filtering To Handle Data Uncertainty And Inconsistency In Mobile Robot Local Positioning". Journal of Intelligent Systems 23.2 (2014): n. pag. Web.
  18. Abdulhafiz, Waleed A., and Alaa Khamis. "Bayesian Approach With Pre- And Post-Filtering To Handle Data Uncertainty And Inconsistency In Mobile Robot Local Positioning". Journal of Intelligent Systems 23.2 (2014): n. pag. Web.
  19. Abdulhafiz, Waleed A., and Alaa Khamis. "Handling Data Uncertainty And Inconsistency Using Multisensor Data Fusion". Advances in Artificial Intelligence 2013 (2013): 1-11. Web.
  20. Leung, H., and Jiangfeng Wu. "Bayesian And Dempster-Shafer Target Identification For Radar Surveillance". IEEE Transactions on Aerospace and Electronic Systems 36.2 (2000): 432-447. Web.
Index Terms

Computer Science
Information Sciences

Keywords

Fusion sensor filter.

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