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SCIENCE CHINA Information Sciences, Volume 63, Issue 7: 170209(2020) https://doi.org/10.1007/s11432-019-2751-4

Data fusion using Bayesian theory and reinforcement learning method

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  • ReceivedAug 30, 2019
  • AcceptedNov 29, 2019
  • PublishedApr 30, 2020

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by Major Projects for Science and Technology Innovation 2030 (Grant No. 2018AA0100800) and Equipment Pre-research Foundation of Laboratory (Grant No. 61425040104).


References

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  • Figure 1

    (Color online) (a) Error histogram; (b) the error curve of random samples.

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    Algorithm 1 Reinforcement learning based Bayesian data fusion algorithm

    Require:The observations $O_1,~O_2,~\ldots,~O_m$, the variances of sensors $\sigma_1,~\sigma_2,~\ldots,~\sigma_m$.

    Output:The fused data.

    Initialize $Q=0$, and set the discount factor $\gamma$;

    for each episode

    for $t=1$ to $m$

    while state $s_t$ is not terminal do

    Initialize state $s_t$;

    $a'\leftarrow$ action in state $s_t$;

    Take action $a'$, calculate reward $r$, and obtain the next available state $s'$;

    Update $Q$ according to (5);

    Calculate the fused data ${\hat~{O}_t}$ based on the Bayesian theory (3);

    $s_t\leftarrow$ optimal new state $s'$;

    end while

    end for

    end for

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