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JIRS
2000

Entropy-Based Markov Chains for Multisensor Fusion

9 years 7 months ago
Entropy-Based Markov Chains for Multisensor Fusion
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 obse...
Albert C. S. Chung, Helen C. Shen
Added 19 Dec 2010
Updated 19 Dec 2010
Type Journal
Year 2000
Where JIRS
Authors Albert C. S. Chung, Helen C. Shen
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