2D-Localization Based on Tracking Antenna Using Artificial Intelligence Mapping (AIM) Approach

Many recent low-range applications require only two-dimensional localization. It is more dependable and simpler to use in real-time low-power and range applications because it requires significantly fewer calculations than three dimensions. As antennas can function as sensors in this system, they ar...

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Veröffentlicht in:IEEE access 2023-01, Vol.11, p.1-1
1. Verfasser: Madany, Yasser M.
Format: Artikel
Sprache:eng
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Zusammenfassung:Many recent low-range applications require only two-dimensional localization. It is more dependable and simpler to use in real-time low-power and range applications because it requires significantly fewer calculations than three dimensions. As antennas can function as sensors in this system, they are regarded as crucial components in the development of localization systems. To avoid a relatively difficult object detection and achieve 360° azimuth plane coverage, the antenna must be as small as possible. The purpose of this study is to design an effective algorithm for the 2D-localization of low-range object positioning using the proposed Artificial Intelligence Mapping (AIM) approach. The proposed AIM approach's modelling and simulation of object localization based on a tracking antenna and the proposed Modified Triangulation Method (MTM) are introduced and analyzed. The design and implementation of the object-localization system were completed. To fulfil the specific aim and suggested AIM approach, the practical 2D-Localization Object System (2D-LOS) uses a commercial identification system. Several trace scenarios for the tracking antenna were illustrated, tested, and compared with samples from other methods in the literature to demonstrate the effectiveness of the proposed localization method for different low-range applications. The proposed AIM approach, which is based on real-world data, has a relatively low resolution and a small error difference.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3280605