This paper describes a 3D reference trajectory generation and tracking control method for a quadcopter. The flying trajectory is a piecewise curve that passes through viapoints at specified times or speeds and has constant acceleration within each interval. The path is first represented as the third degree polynomials, and to overcome demerits of the C-splines, a numeric algorithm is suggested and implemented. The feasibility of the generated trajectory is checked by nonlinear dynamics model of a quadcopter. After executing several flying experiments for a quadcopter, tracking errors are evaluated.

SNUGLITE (Seoul National University Global navigation satellite system Laboratory satellITE) is a two-unit cube satellite (CubeSat) with dimensions 10 × 10 × 23 cm that requires an attitude system for missions and ground station telecommunication. A linear-quadratic-Gaussian-based optimal attitude system for the CubeSat platform has been developed using low-cost sensors, with the in-orbit verification of the attitude system being is one of main study objectives. Since launch, the SNUGLITE CubeSat has continuously broadcast in-orbit status information. In this study, a methodology for the analysis of in-orbit attitude estimation results using received data is presented, and this was achieved by comparing two sun-pointing vectors, i.e., the sun-pointing vector calculated using estimated attitude with the positions of the sun and the satellite and the reference vector generated by the power levels of the solar panels. Because the satellite position was required for the attitude analysis, the verification of the performance of the own-developed on-board Global Positioning System (GPS) receiver is also briefly described. Analyses indicate that the attitude estimation of the SNUGLITE CubeSat has achieved an in-orbit real-time pointing accuracy with a root mean square of 6.1°.

In recent years, research on increasing the spatial resolution and enhancing the quality of satellite images using the deep learning-based super-resolution (SR) method has been actively conducted. In a remote sensing field, conventional SR methods required high-quality satellite images as the ground truth. However, in most cases, high-quality satellite images are difficult to acquire because many image distortions occur owing to various imaging conditions. To address this problem, we propose an adaptive image quality modification method to improve SR image quality for the KOrea Multi-Purpose Satellite-3 (KOMPSAT-3). The KOMPSAT-3 is a high performance optical satellite, which provides 0.7-m ground sampling distance (GSD) panchromatic and 2.8-m GSD multi-spectral images for various applications. We proposed an SR method with a scale factor of 2 for the panchromatic and pan-sharpened images of KOMPSAT-3. The proposed SR method presents a degradation model that generates a low-quality image for training, and a method for improving the quality of the raw satellite image. The proposed degradation model for low-resolution input image generation is based on Gaussian noise and blur kernel. In addition, top-hat and bottom-hat transformation is applied to the original satellite image to generate an enhanced satellite image with improved edge sharpness or image clarity. Using this enhanced satellite image as the ground truth, an SR network is then trained. The performance of the proposed method was evaluated by comparing it with other SR methods in multiple ways, such as edge extraction, visual inspection, qualitative analysis, and the performance of object detection. Experimental results show that the proposed SR method achieves improved reconstruction results and perceptual quality compared to conventional SR methods.

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