Indoor trajectory mapping is essential for applications such as health status monitoring, context-aware computing, and tracking systems. These applications often require accurate location information in environments where the Global Navigation Satellite System (GNSS) is unavailable or unreliable. Infrastructure-free approaches, such as inertial sensor-based Pedestrian Dead Reckoning (PDR), have gained popularity due to their ease of deployment and low cost. However, a key limitation of PDR is not only its susceptibility to cumulative drift over time but also its lack of absolute position references, which prevents reliable alignment of the trajectory with real-world coordinates. To address this challenge, we propose an activity-driven audio-IMU fusion solution that uses Factor Graph Optimization (FGO) to accurately reconstruct indoor walking trajectories on a room-scale floor plan. Specifically, we use two fixed microphone arrays to estimate absolute coordinates from activity-driven audio events. These events act as sparse, high-confidence anchors to map PDR trajectories. We validate our system through experiments in a 6 m × 6 m room. The proposed fusion solution achieves a mean positioning error of 0.31 m, with 68% and 95% of errors below 0.38 m and 0.53 m, respectively. The results demonstrate the potential of sparse, activity-driven audio anchoring to improve infrastructure-free indoor trajectory mapping.
This paper presents the implementation of a localization system in a senior living residence located in a town in the province of Guadalajara, Spain. The system integrates precise location tracking using Ultra-Wideband (UWB) technology in the common areas of the residence, and symbolic positioning via millimeter-wave (mmWave) radar modules installed in private rooms. In UWB-monitored areas, users carry a wearable UWB tag, while in mmWave-radar monitored rooms, a device-free approach is adopted. The localization system is designed to be scalable, easy to deploy, and to support remote maintenance and management, facilitating long-term operation with minimal on-site intervention. This paper provides a detailed description of the devices used, the deployment process, and the overall system setup. Furthermore, by leveraging open-source tools, efficient data management has been achieved. Finally, raw data collected from the implemented system are presented graphically. These data will support future development of tools to detect behavioral anomalies early, which may indicate physical or cognitive decline among the monitored individuals. The collected data are also valuable for identifying daily behavioral patterns and routines, and for detecting deviations that could signal the onset of conditions associated with physical and/or cognitive impairment.
This paper introduces a three-dimensional positioning platform designed to enhance the quality of Korea Telecom's emergency location service by providing floor-level accuracy. Since 2019, the Korea Communications Commission has publicly announced the test results for the positioning quality of mobile operators’ emergency location services. These results are classified by three positioning methods—GNSS, Wi-Fi, and cellular network—in terms of accuracy and time. The main guideline for positioning accuracy is as follows 50 metres horizontal positioning accuracy and 30 seconds total positioning time limit. More recently, the Federal Communications Commission's guideline for vertical positioning requirements, specifying 3 metres of Z-axis accuracy with 90% confidence, has been a subject of discussion among many stakeholders. In order to achieve these quality requirements for emergency location services, Korea Telecom is setting a new challenging accuracy target and working to provide enhanced emergency location services from the perspective of providing end-to-end location information using a floor-level accuracy technique of three-dimensional positioning platform.
This study presents a neural network-based method for static Bluetooth Low Energy (BLE) localization, using phase measurements and carrier frequency per data packet, captured from a single (or multiple) multi-antenna receiver(s). Deterministic phase-based techniques fail to accurately estimate the position of a tag using a single multi-antenna receiver with closely spaced antennas. This is due to the random and unknown carrier phase offset (CPO) at each antenna element, introduced during BLE’s inherent frequency hopping between consecutive packets. Our long short-term memory (LSTM) model learns to handle the unknown distribution of CPO and suppress phase noise, enabling robust localization with just one multi-antenna receiver. Among the tested architectures, the LSTM model showed notable resilience to phase noise and achieved higher accuracy during high-rate tag transmissions compared to the feedforward (FF) and convolutional neural network (CNN) models. All models were trained on simulated or real data and tested on real data from various environments, where they outperformed deterministic techniques—even in cases where those techniques either excelled or failed to estimate the tag’s position.
