ChirpTracker: A Precise-Location-Aware System for Acoustic Tag Using Single Smartphone

Abstract

The increasing interest in loss prevention devices using the Internet of Things, has been driven by the convenience, low cost, and low power consumption of these devices. However, the existing technologies cannot achieve a balance between high availability over a wide area with a single smartphone and precise location awareness. In this paper, a novel Precise-Location-Aware method that integrates acoustic technology and pedestrian dead reckoning, ChirpTracker, is proposed which most smartphones support without auxiliary equipment. This system can provide wide coverage (30m) and is suitable for many scenarios such as searching for cars in underground parking or finding items indoors. ChirpTracker can detect the distance between the smartphone and a lost tag in real time using acoustic signals, it can monitor the relative position change of the smartphone based on deep learning based pedestrian dead reckoning (PDR) and update relative positioning of the lost tag though the observation from single base-station in motion. A technology that combines the local least squares method (LSM) and particle filter (PF) improves the convergence and the robustness of ChirpTracker through an identification strategy for a mirror position. This method was validated in experiments conducted in actual environments. The results demonstrate the effectiveness and positioning accuracy of ChirpTracker.