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Journal of Intelligent and Robotic Systems 29
(2):161-189, October 2000. © Kluwer Academic Publishers
Entropy-Based Markov Chains for Multisensor FusionA. C. S.
Chung Department of Computer Science, The Hong Kong University
of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; e-mail:
helens@cs.ust.hk
H. C. Shen Department of Computer
Science, The Hong Kong University of Science and Technology, Clear Water
Bay, Kowloon, Hong Kong; e-mail:
helens@cs.ust.hk
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.
Keywords decision making, entropy, Markov
chains, multisensor fusion, uncertainty
ISSN 0921-0296
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