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Entropy-Based Markov Chains for Multisensor Fusion

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Abstract

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.

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Authors and Affiliations

  1. Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

    A. C. S. Chung & H. C. Shen

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  1. A. C. S. Chung
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  2. H. C. Shen
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Chung, A.C.S., Shen, H.C. Entropy-Based Markov Chains for Multisensor Fusion. Journal of Intelligent and Robotic Systems 29, 161–189 (2000). https://doi.org/10.1023/A:1008138126506

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