DocumentCode
1600160
Title
Statistical information fusion criteria for multi-sensory systems
Author
Lin, Hong-Dar ; Chang, C. Alec
Author_Institution
Dept. of Ind. Eng., Tunghai Univ., China
fYear
1995
Firstpage
535
Lastpage
539
Abstract
Most current information fusion techniques focus on adding all information sources, and then observe the operation of a fused system. These methods generally do not concern whether a new sensor source would enhance system performances beforehand. The statistical meta-analysis offers a set of quantitative techniques that permit synthesizing a variety of independent information sources. Using the statistical meta-analysis, this paper presents a method that can provide fusion criteria to foretell the effect of adding a new information source in terms of statistical type I errors before the source is actually combined. This method is then implemented as an illustration
Keywords
sensor fusion; statistical analysis; information sources synthesis; multi-sensory systems; quantitative techniques; statistical information fusion criteria; statistical meta-analysis; statistical type I errors; Expert systems; Feedforward neural networks; Fuzzy logic; Industrial engineering; Neural networks; Production systems; Sensor fusion; Sensor systems; Statistical analysis; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Automation and Control: Emerging Technologies, 1995., International IEEE/IAS Conference on
Conference_Location
Taipei
Print_ISBN
0-7803-2645-8
Type
conf
DOI
10.1109/IACET.1995.527615
Filename
527615
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