论文标题
混合室内室内定位通过增强学习的信息融合
Hybrid Indoor Localization via Reinforcement Learning-based Information Fusion
论文作者
论文摘要
该论文是由智能城市（SC）概念在全球城市化的未来管理中的重要性所激发的。在所有物联网（IoT）的通信技术中，蓝牙低能（BLE）在全市范围内的决策和服务中起着至关重要的作用。但是，接收的信号强度指标（RSSI）的极端波动使该技术在不断变化的SC环境中以可接受的精度为可接受的解决方案。 BLE v.5.1的最新版本引入了通过基于到达角度（AOA）的方向查找方法来跟踪用户的更好可能性，这更可靠。仍然有一些基本问题要解决。现有作品主要集中于实施独立模型，忽视潜在的融合策略。该论文通过将AOA与基于RSSI的粒子过滤和基于RSSI的粒子滤器（IMU）基于基于RSSI的粒子滤器（IMU）基于基于RSSI的粒子滤清器（IMU）基于基于AOA的框架（RL-IFF）提出了新的增强学习（RL）信息融合框架（RL-IFF）。通过一组全面的实验来评估所提出的RL-IFF解决方案，这些实验表明与同行相比，表现出色的性能。
The paper is motivated by the importance of the Smart Cities (SC) concept for future management of global urbanization. Among all Internet of Things (IoT)-based communication technologies, Bluetooth Low Energy (BLE) plays a vital role in city-wide decision making and services. Extreme fluctuations of the Received Signal Strength Indicator (RSSI), however, prevent this technology from being a reliable solution with acceptable accuracy in the dynamic indoor tracking/localization approaches for ever-changing SC environments. The latest version of the BLE v.5.1 introduced a better possibility for tracking users by utilizing the direction finding approaches based on the Angle of Arrival (AoA), which is more reliable. There are still some fundamental issues remaining to be addressed. Existing works mainly focus on implementing stand-alone models overlooking potentials fusion strategies. The paper addresses this gap and proposes a novel Reinforcement Learning (RL)-based information fusion framework (RL-IFF) by coupling AoA with RSSI-based particle filtering and Inertial Measurement Unit (IMU)-based Pedestrian Dead Reckoning (PDR) frameworks. The proposed RL-IFF solution is evaluated through a comprehensive set of experiments illustrating superior performance compared to its counterparts.
