Accurate 3D Mapping Algorithm for Flexible Antennas

Abstract

This work addresses the problem of performing an accurate 3D mapping of a flexible antenna surface. Consider a high-gain satellite flexible antenna; even a submillimeter change in the antenna surface may lead to a considerable loss in the antenna gain. Using a robotic subreflector, such changes can be compensated for. Yet, in order to perform such tuning, an accurate 3D mapping of the main antenna is required. This paper presents a general method for performing an accurate 3D mapping of marked surfaces such as satellite dish antennas. Motivated by the novel technology for nanosatellites with flexible high-gain antennas, we propose a new accurate mapping framework which requires a small-sized monocamera and known patterns on the antenna surface. The experimental result shows that the presented mapping method can detect changes up to 0.1millimeter accuracy, while the camera is located 1 meter away from the dish, allowing an RF antenna optimization for Ka and Ku frequencies. Such optimization process can improve the gain of the flexible antennas and allow an adaptive beam shaping. The presented method is currently being implemented on a nanosatellite which is scheduled to be launched at the end of 2018.

An Analysis of the Implementation of Edge Detection Operators in FPGA

“…The evolution of computing systems made possible the development of new applications. Among the emerging applications, we can highlight those using computer vision systems such as unmanned underwater vehicles [1], 3D environment mapping [2], and stereo image processing for robots [3]. A growing area of research that has a high interest in fast image processing at low cost is that of autonomous electric vehicles, as evidenced in [4].…”