Securing Fast and High-Precision Localization for Shallow Underground Explosive Source: A Curiosity-Driven Deep Reinforcement Learning Approach

Securing Fast and High-Precision Localization for Shallow Underground Explosive Source: A Curiosity-Driven Deep Reinforcement Learning Approach

Shallow underground explosive source localization technology is a key technology in the field of underground space localization. The existing approaches mainly aim to improve the localization accuracy, but need to deploy enormous sensors in the monitoring area, and rely on a large number of backend...

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Bibliographic Details
Main Authors: Dan Wu, Liming Wang, Jian Li, Meiyan Liang, Yunpeng Kang, Qi Jiao
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Curiosity-driven deep reinforcement learning (DRL)
dueling neural network
underground source localization
vibration energy field
Online Access:https://ieeexplore.ieee.org/document/10387674/

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