LiDAR Integrated High-Capacity IR OWC System with Abilities of Localization and Link Alignment

Abstract

By using narrow infrared (IR) beams, optical wireless communication (OWC) system can realize ultra-high capacity and high-privacy transmission. However, due to the point-to-point connection approach, a high-accuracy localization system and beam-steering antenna (BSA) are required to steer the signal beam to user terminals. In addition, to achieve link alignment in the receiver, the BSA needs to be within the limited receiver field of view (FoV). This problem greatly limits the practical application of high-capacity IR optical wireless communication and has not been well solved yet. In this paper, we proposed an indoor beam-steering IR OWC system with high-accuracy and calibration-free localization ability by employing a coaxial frequency modulated continuous wave (FMCW) light detection and ranging (LiDAR) system. In the meantime, benefit from the mm-level ranging accuracy of the LiDAR system, a feasible approach to deal with the link alignment issue for practical applications is firstly demonstrated. With the assistance of the LiDAR system, we experimentally achieved the localization of user terminals with a 0.038-degree localization accuracy, link alignment at the receiver and an error-free on-off keying (OOK) downlink transmission of 20 Gb/s in free space at 3-m distance is demonstrated.

Displacement Measurement of Urban Ramp Bridge Model Using Monocular High-Speed Camera

“…In order to obtain high-precision offset information of the target, the artificial marker designed in this paper is shown in figure 1, which is attached to the surface of the measured target, and the displacement of the artificial marker is considered as the displacement of the structural target point in the measurement for analysis. [11] to edge-fit the recognition results, then uses the nonlinear optimal Levenberg-Marquardt method [12] to recursively search for the circle center based on the standard equation of the ellipse, to determine the subpixel-level circle center location with subpixel accuracy better than 1/ 20 pixels. The target neighborhood block-based tracking matching is to set the neighborhood in the deformation time series image with the initial position of each target separately, and to perform automatic target identification matching within each neighborhood.…”

“…The automated recognition includes region of interest window image chunking and binarization, image morphology processing, and constraint recognition based on pixel block circle features. Subpixel-level localization first uses the overall least squares method[11] to edge-fit the recognition results, then uses the nonlinear optimal Levenberg-Marquardt method[12] to recursively search for the circle center based on the standard equation of the ellipse, to determine the subpixel-level circle center location with subpixel accuracy better than 1/ 20 pixels. The target neighborhood block-based tracking matching is to set the neighborhood in the deformation time series image with the initial position of each target separately, and to perform automatic target identification matching within each neighborhood.…”