Handheld Antenna Assessment Based on CV Ego-motion Position and Orientation Estimation of a Mobile Device
Date
2020-01-21
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Abstract
5G wireless communication features the development of millimetre-wave antenna array and the beamforming technology. 5G antenna array will require regular calibration as its performance depends on the physical orientation and movement of the device, the nearfield electromagnetic wave scattering which changes over time, as well as the body absorption factors. Unfortunately, the conventional calibration method that needs to be carried out in an anechoic chamber is very inconvenient for performing frequent calibration, this method also lacks the flexibility to emulate the daily usage scenarios accurately. A potentially better solution is to devise a method to compute the dynamics of the device movement and then use it in the antenna array calibration. The underlying hypothesis is that if the pose of the mobile device can be estimated with sub-millimetre accuracy over a time epoch, then a quantitative assessment of the array performance would be possible. This thesis made use of a single antenna to demonstrate the alignment between the pose estimates obtained using Computer Vision techniques and the antenna phase measurements, thus providing a feasible solution to its future application in the assessment of the 5G antenna array.
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Keywords
Computer Vision, Antenna, Pose Estimate
Citation
Guo, Y. R. (2020). Handheld Antenna Assessment Based on CV Ego-motion Position and Orientation Estimation of a Mobile Device (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.