A new adaptive weighted fusion algorithm for multi-sensor tracking

Presents a technique for the online adaptive weighted fusion algorithm for multi-sensor tracking. The algorithm consists three steps: (i) estimation of the variance of the sensor's measurements noise using statistical theory; (ii) adjustment of the fused sensor's weight coefficient accordi...

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Bibliographic Details
Published inProceedings. International Conference on Machine Learning and Cybernetics Vol. 1; pp. 285 - 287 vol.1
Main Authors Jiang Zhao, Shi-Qiang Hu
Format Conference Proceeding
LanguageEnglish
Published IEEE 2002
Subjects
Acceleration
Bayesian methods
Coordinate measuring machines
Cybernetics
Estimation theory
Filters
Fuzzy logic
Fuzzy sets
Sensor systems
Target tracking
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Summary:Presents a technique for the online adaptive weighted fusion algorithm for multi-sensor tracking. The algorithm consists three steps: (i) estimation of the variance of the sensor's measurements noise using statistical theory; (ii) adjustment of the fused sensor's weight coefficient according to the sensor's noise variance change; (iii) prediction of the next target position using the "current" statistical model and a Kalman filter method. Computer simulation results are presented to demonstrate the performance of this approach.
ISBN:9780780375086
0780375084
DOI:10.1109/ICMLC.2002.1176758