强化学习
传感器融合
稳健性(进化)
计算机科学
先验与后验
融合
人工智能
可靠性(半导体)
样条插值
机器学习
数据挖掘
计算机视觉
哲学
语言学
生物化学
化学
功率(物理)
物理
认识论
量子力学
双线性插值
基因
作者
Tongle Zhou,Mou Chen,Jie Zou
标识
DOI:10.1109/jas.2020.1003180
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 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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