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A Review of Data Fusion Techniques

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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 and Hemalatha M.. A Review of Data Fusion Techniques. International Journal of Computer Applications 167(7):37-41, June 2017. BibTeX

@article{10.5120/ijca2017914318,
	author = {Afnan Alofi and Anwaar Alghamdi and Razan Alahmadi and Najla Aljuaid and Hemalatha M.},
	title = {A Review of Data Fusion Techniques},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {7},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {37-41},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume167/number7/27786-2017914318},
	doi = {10.5120/ijca2017914318},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, 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.

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Keywords

Fusion, sensor, filter.