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
Link To Document :
بازگشت