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Volume 7 Issue 6
Oct.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Tongle Zhou, Mou Chen and Jie Zou, "Reinforcement Learning Based Data Fusion Method for Multi-Sensors," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1489-1497, Nov. 2020. doi: 10.1109/JAS.2020.1003180
Citation: Tongle Zhou, Mou Chen and Jie Zou, "Reinforcement Learning Based Data Fusion Method for Multi-Sensors," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1489-1497, Nov. 2020. doi: 10.1109/JAS.2020.1003180

Reinforcement Learning Based Data Fusion Method for Multi-Sensors

doi: 10.1109/JAS.2020.1003180
Funds:  This work was supported in part by the Major Projects for Science and Technology Innovation 2030 (2018AA0100800), the Equipment Pre-research Foundation of Laboratory (61425040104), the Joint Fund of China Electronics Technology for Equipment Preresearch (6141B08231110a), and the Funding for Short Visit Program of Nanjing University of Aeronautics and Astronautics (NUAA) (190915DF03)
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  • In order to improve detection system robustness and reliability, multi-sensors fusion is used in modern air combat. In this paper, a data fusion method based on reinforcement learning is developed for multi-sensors. Initially, the cubic B-spline interpolation is used to solve time alignment problems of multi-source data. Then, the reinforcement learning based data fusion (RLBDF) method is proposed to obtain the fusion results. With the case that the priori knowledge of target is obtained, the fusion accuracy reinforcement is realized by the error between fused value and actual value. Furthermore, the Fisher information is instead used as the reward if the priori knowledge is unable to be obtained. Simulations results verify that the developed method is feasible and effective for the multi-sensors data fusion in air combat.

     

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    Highlights

    • A data pre-processing method is raised before data fusion, which could solve the time alignment problem.
    • To improve the accuracy of data fusion system, a data fusion approach based on reinforcement learning is designed by multi-sensors weight adjustment.
    • In the case without prior knowledge, the reinforcement learning based data fusion is realized by the Fisher information of observations.

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