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

基于智能手机的室内定位在研究和工业领域都引起了相当大的关注。然而在复杂的定位环境中,定位的准确性和鲁棒性仍然是具有挑战性的问题。考虑到行人航位推算(PDR,pedestrian dead reckoning)算法被广泛配备在最近的智能手机上,提出了一种基于双延迟深度确定性策略梯度(TD3,twin delayed deep deterministic policy gradient)的室内定位融合方法,该方法集成了Wi-Fi信息和PDR数据,将PDR的定位过程建模为马尔可夫过程并引入了智能体的连续动作空间。最后,与3个最先进的深度Q网络(DQN,deep Q network)室内定位方法进行实验。实验结果表明,该方法能够显著减少定位误差,提高定位准确性。

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 environments.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 introduced 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.