物联网学报2024，Vol.8Issue(1)：40-48,9.DOI:10.11959/j.issn.2096-3750.2024.00358
基于连续动作空间深度强化学习的多数据融合室内定位方法
Multi-data fusionaided indoor localization based on continuous action space deep reinforcement learning
摘要
Abstract
Significant attention has been paid to indoor localization using smartphones in both research and industry.However,the accuracy and robustness of localization remain challenging issues,particularly in complex indoor environ-ments.In light of the prevalent incorporation of pedestrian dead reckoning(PDR)devices in contemporary smartphones,an advanced indoor localization fusion method,anchored in the twin delayed deep deterministic policy gradient(TD3)framework,was proposed.In this approach,a seamless integration of Wi-Fi information and PDR data was achieved.The localization process of PDR was modeled as a Markov process,and a comprehensive continuous action space was intro-duced for the agent.To evaluate the performance of the proposed method,experiments were conducted and this approach was compared with three state-of-the-art deep Q network(DQN)based indoor localization methods.The experimental results demonstrate that the proposed method significantly reduces localization errors and enhances overall localization accuracy.关键词
Wi-Fi/行人航位推算/室内定位/双延迟深度确定性策略梯度/深度强化学习Key words
Wi-Fi/pedestrian dead reckoning/indoor localization/twin delayed deep deterministic policy gradient/deep reinforcement learning分类
信息技术与安全科学引用本文复制引用
陈雪晨,易嘉旋,王霭祥,邓晓衡..基于连续动作空间深度强化学习的多数据融合室内定位方法[J].物联网学报,2024,8(1):40-48,9.基金项目
国家自然科学基金项目(No.62172441) （No.62172441）
四川省重点研发计划(No.2023YFG0120) The National Natural Science Foundation of China(No.62172441),The Key Research and Development Pro-gram of Sichuan Province(No.2023YFG0120) （No.2023YFG0120）
