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

Abstract

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 narrowband signals because of multipath propagation. In this paper, we design a positioning system using narrowband 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.

Keywords

• Indoor positioning
• Internet of Things
• ensemble learning
• software defined radio