Introduction to operations research, 4th ed.
Introduction to operations research, 4th ed.
Sensor models and multisensor integration
International Journal of Robotics Research - Special Issue on Sensor Data Fusion
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Visual surveillance in a dynamic and uncertain world
Artificial Intelligence - Special volume on computer vision
Generalized polyhedral object recognition and localization using crossbeam sensing
International Journal of Robotics Research
Mathematical Techniques in Multisensor Data Fusion
Mathematical Techniques in Multisensor Data Fusion
Data Fusion for Sensory Information Processing Systems
Data Fusion for Sensory Information Processing Systems
Bayes networks for sonar sensor fusion
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A Multi-Level Strategy for Energy Efficient Data Aggregation in Wireless Sensor Networks
Wireless Personal Communications: An International Journal
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This paper proposes an entropy based Markov chain (EMC) fusion technique and demonstrates its applications in multisensor fusion. Self-entropy and conditional entropy, which measure how uncertain a sensor is about its own observation and joint observations respectively, are adopted. We use Markov chain as an observation combination process because of two major reasons: (a) the consensus output is a linear combination of the weighted local observations; and (b) the weight is the transition probability assigned by one sensor to another sensor. Experimental results show that the proposed approach can reduce the measurement uncertainty by aggregating multiple observations. The major benefits of this approach are: (a) single observation distributions and joint observation distributions between any two sensors are represented in polynomial form; (b) the consensus output is the linear combination of the weighted observations; and (c) the approach suppresses noisy and unreliable observations in the combination process.