Reinforcement Learning Based Data Fusion Method for Multi-Sensors

Tongle Zhou; Mou Chen; Jie Zou

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Reinforcement Learning Based Data Fusion Method for Multi-Sensors

机译：基于增强学习的多传感器数据融合方法

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摘要

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 multisource 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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《自动化学报（英文版）》 |2020年第6期|1489-1497|共9页
作者
Tongle Zhou; Mou Chen; Jie Zou;
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作者单位

College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106 China;

College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106 China;

Science and Technology on Electron-Optic Control Laboratory Luoyang Institute of Electro-Optical Equipment of Avic Luoyang 471023 China;

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正文语种 eng
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Reinforcement Learning Based Data Fusion Method for Multi-Sensors

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