DocumentCode
2046189
Title
A neural approach to data fusion
Author
Chowdhury, Fahmida N.
Author_Institution
Dept. of Electr. Eng., Michigan Technol. Univ., Houghton, MI, USA
Volume
3
fYear
1995
fDate
21-23 Jun 1995
Firstpage
1693
Abstract
A neural approach to data fusion is proposed. We assume that remote sites process local sensor data, and the fusion center does not have covariance information. A neural network consisting of one neuron for each component of the measurement vector is proposed as the fusion center, provided it has been trained with past data. This is an alternative to the standard approach of estimating the covariances explicitly. To demonstrate the idea, some simulation results are shown
Keywords
learning (artificial intelligence); neural nets; sensor fusion; covariances; data fusion; local sensor data; measurement vector; neural network; Covariance matrix; Estimation error; Filters; Genetic expression; History; Neural networks; Neurons; Sensor fusion; State estimation; Target tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, Proceedings of the 1995
Conference_Location
Seattle, WA
Print_ISBN
0-7803-2445-5
Type
conf
DOI
10.1109/ACC.1995.529797
Filename
529797
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