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Efficient sensor selection for active information fusion.

Yongmian Zhang¹, Qiang Ji

¹Department of Electrical, Computer and SystemsEngineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. zhangy@ecse.rpi.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|October 23, 2009

Summary

This summary is machine-generated.

This study introduces an efficient method for selecting cost-effective sensors for active information fusion. The new approach uses sensor synergy graphs to near-optimally identify the best sensor subsets, improving decision-making.

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Area of Science:

Computer Science
Electrical Engineering
Information Theory

Background:

Active information fusion frameworks utilize dynamic Bayesian networks for enhanced data integration.
Efficient sensor subset selection is critical for cost-effective and decision-relevant active information fusion.
Evaluating all sensor subsets for optimal selection is computationally intractable using traditional information-theoretic criteria.

Purpose of the Study:

To develop an efficient method for identifying the most informative and cost-effective sensor subsets for active information fusion.
To address the computational intractability of exhaustive sensor subset evaluation.
To propose a novel approach for quantifying sensor synergy and its impact on information gain.

Main Methods:

A new quantitative measure for sensor synergy was developed.
A sensor synergy graph was constructed based on the proposed measure.
An alternative measure to multisensor mutual information was introduced to characterize sensor information gain.
An approximated nonmyopic sensor selection algorithm was proposed for efficient subset identification.

Main Results:

The proposed sensor synergy graph facilitates a more efficient characterization of sensor information gain.
The approximated nonmyopic sensor selection method achieves near-optimal results.
Simulation studies validated the performance and efficiency of the proposed sensor selection technique.

Conclusions:

The developed sensor synergy-based approach provides an efficient and effective solution for sensor selection in active information fusion.
The method overcomes the computational limitations of traditional approaches, enabling practical application.
This work contributes to the advancement of intelligent sensor systems and data fusion techniques.