Trajectory mapping through channel state information by triangulation method and fine-tuning

• Published in: Journal of engineering and applied science (Online) 2024-12, Vol.71 (1), p.196-36, Article 196
• Main Authors: Abuhoureyah, Fahd, Wong, Yan Chiew, Mohd Isira, Ahmad Sadhiqin
• Format: Article
• Language: English
• Subjects: Accuracy; Adaptation; Algorithms; Channel state information; Civil Engineering; Cost analysis; Electrical Engineering; Engineering; Engineering Mathematics; Estimates; Fine tuning; Global positioning systems; GPS; Indoor environments; Industrial Chemistry/Chemical Engineering; Infrastructure; Localization; Mapping; Mechanical Engineering; Methods; Propagation; Robotics; Spatial resolution; System effectiveness; Tracking devices; Tracking systems; Trajectories; Trajectory tracking; Triangulation; WiFi sensing
• ISSN: 1110-1903; 2536-9512
• DOI: 10.1186/s44147-024-00531-6

Trajectory mapping techniques have widespread applications in diverse fields, including robotics, localization, smart environments, gaming, and tracking systems. However, existing free devices encounter challenges in representing trajectories, thereby limiting the effectiveness of applications such as robotics, localization, and tracking systems. The imprecise mappings generated by these methods lead to suboptimal performance and unreliable results. The proposed approach leverages WiFi sensing through channel state information (CSI), triangulation techniques, and a fine-tuning mechanism to enhance trajectory precision within indoor environment trajectory mapping. The proposed solution employs a domain adapter fine-tuning technique to enable location-independent tracking via CSI, minimizing errors. The use of CSI MIMO signals for trajectory mapping offers enhanced spatial resolution, robust multipath handling, and improved accuracy in tracking movement by leveraging multiple antenna channels and exploiting the rich information embedded in signal reflections and scattering, while triangulation aids in accurately determining the location of objects or targets. Furthermore, incorporating a fine-tuning mechanism refines the generated trajectories. The findings demonstrate substantial enhancements in mapping precision, with an accuracy of 95.5% in tracking 13 paths within the new domain. These results underscore the effectiveness of the proposed approach in overcoming the limitations of existing methods and achieving highly accurate trajectory mapping.