Indoor positioning techniques, owing to received signal strength indicator (RSSI)-based sensors, can provide useful trajectory-based services. These services include user movement analytics, next-to-visit recommendation, and hotspot detection. However, the value of RSSI is often disturbed due to obstacles in indoor environment, such as doors, walls, and furnitures. Therefore, many indoor positioning techniques still extract an invalid trajectory from the disturbed RSSI. An invalid trajectory contains distant or impossible consecutive positions within a short time, which is unlikely in a real-world scenario. In this study, we enhanced indoor positioning techniques with movement constraints on BLE (Bluetooth Low Energy) RSSI data to prevent an invalid semantic indoor trajectory. The movement constraints ensure that a predicted semantic position cannot be far apart from the previous position. Furthermore, we can extend any indoor positioning technique using these movement constraints. We conducted comprehensive experimental studies on real BLE RSSI datasets from various indoor environment scenarios. The experimental results demonstrated that the proposed approach effectively extracts valid indoor semantic trajectories from the RSSI data.

中文翻译：

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将运动约束应用于基于BLE RSSI的室内定位，以提取有效的语义轨迹。

由于基于接收信号强度指示器（RSSI）的传感器，室内定位技术可以提供有用的基于轨迹的服务。这些服务包括用户移动分析，下次访问推荐和热点检测。但是，由于室内环境中的障碍物（例如门，墙壁和家具），RSSI的值通常会受到干扰。因此，许多室内定位技术仍然从受干扰的RSSI中提取无效轨迹。无效的轨迹包含短时间内的遥远或不可能的连续位置，这在现实世界中不太可能发生。在这项研究中，我们通过对BLE（蓝牙低功耗）RSSI数据进行运动约束来增强室内定位技术，以防止室内语义轨迹无效。运动约束确保了预测的语义位置不会与先前的位置相距甚远。此外，我们可以利用这些运动约束来扩展任何室内定位技术。我们对来自各种室内环境场景的真实BLE RSSI数据集进行了全面的实验研究。实验结果表明，该方法有效地从RSSI数据中提取了有效的室内语义轨迹。