A Narrow-Band Indoor Positioning System by Fusing Time and Received Signal Strength via Ensemble Learning

• Published in: IEEE access 2018-01, Vol.6, p.9936-9950
• Main Authors: Li, Zan, Braun, Torsten, Zhao, Xiaohui, Zhao, Zhongliang, Hu, Fengye, Liang, Hui
• Format: Article
• Language: English
• Subjects: Algorithms; Bandwidth; Distance measurement; Ensemble learning; Fingerprinting; Indoor positioning; Internet of Things; Narrowband; Object recognition; Pattern matching; Regression models; Signal processing algorithms; Signal strength; Smart buildings; Software algorithms; software defined radio; Software radio; Synchronism; Wireless fidelity; ZigBee
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2794337

The Internet of Things (IoT) is an emerging paradigm to integrate the physical world into cyber systems by connecting various devices, which sense and control the surrounding objects. For the upcoming IoT era, narrow-band signals will play an important role in the exchange of sensor data and control signals in IoT networks. Indoor positioning is critical for many IoT applications, such as asset tracking, smart buildings, smart e-health, and smart cities, but it is very challenging to achieve accurate indoor positioning with narrow-band signals because of multipath propagation. In this paper, we design a positioning system using narrow-band signals, particularly ZigBee signals, based on an enhanced fingerprinting algorithm by fusing received signal strength (RSS) and time information. We first investigate the feasibility of time-based fingerprinting by handling the challenges as synchronization compensation among anchor nodes and design of sub-sample timestamps with a resolution of nanoseconds. Second, we design a feature-based fusion approach to fuse and standardize time and RSS fingerprints. Third, we adopt a random forest regression model to design an enhanced pattern matching algorithm for fingerprinting. We implement the proposed algorithms with software-defined radio techniques. In the given experiment results, we find that the positioning system achieves a mean positioning accuracy of 1.61 m, which represents a 36.1% improvement over traditional RSS-based fingerprinting.