• Journal of Astronomy and Space Sciences
• /
• v.40 no.2
• /
• pp.79-88
• /
• 2023

On Aug. 4, 2022, at 23:08:48 (UTC), the Korea Pathfinder Lunar Orbiter (KPLO), also known as Danuri, was launched using a SpaceX Falcon 9 launch vehicle. Currently, KPLO is successfully conducting its science mission around the Moon. The National Aeronautics and Space Administration (NASA)'s Deep Space Network (DSN) was utilized for the successful flight operation of KPLO. A great deal of joint effort was made between the Korea Aerospace Research Institute (KARI) and NASA DSN team since the beginning of KPLO ground system design for the success of the mission. The efficient utilization and management of NASA DSN in deep space exploration are critical not only for the spacecraft's telemetry and command but also for tracking the flight dynamics (FD) operation. In this work, the top-level DSN interface architecture, detailed workflows, DSN support levels, and practical lessons learned from the joint team's efforts are presented for KPLO's successful FD operation. Due to the significant joint team's efforts, KPLO is currently performing its mission smoothly in the lunar mission orbit. Through KPLO cooperative operation experience with DSN, a more reliable and efficient partnership is expected not only for Korea's own deep space exploration mission but also for the KARI-NASA DSN joint support on other deep space missions in the future.

• The Bulletin of The Korean Astronomical Society
• /
• v.45 no.1
• /
• pp.46.1-46.1
• /
• 2020

DEEP-South (DEep Ecliptic Patrol of the Southern Sky) team will start the 2nd phase of KMTNet observation in Oct 2020. The DEEP-South observation mainly consists of three survey modes: (1) Activity survey (AS) that aims at finding active phenomena of small Solar System bodies. (2) Light curve survey (LS) targets to discover and characterize light variations of asteroids. And (3) Deep drilling survey (DS) focuses on the objects beyond the orbit of Jupiter (Centaurus and trans-Neptunian objects) as well as near Earth asteroids. For asteroid family (AF) studies and target of opportunity (TO) observations for urgent photometric follow-up, targeted mode will also be used. DEEP-South team is awarded 7.0% of the telescope time at each site every year from Oct 2020 to Sep 2023 in the 2nd phase of KMTNet operation which corresponds to about 75 full nights a year for the network. In this presentation, we will introduce our survey strategy and observation plan.

• Journal of Astronomy and Space Sciences
• /
• v.34 no.3
• /
• pp.213-223
• /
• 2017

The deep space orbit determination software (DSODS) is a part of a flight dynamic subsystem (FDS) for the Korean Pathfinder Lunar Orbiter (KPLO), a lunar exploration mission expected to launch after 2018. The DSODS consists of several sub modules, of which the orbit determination (OD) module employs a weighted least squares algorithm for estimating the parameters related to the motion and the tracking system of the spacecraft, and subroutines for performance improvement and detailed analysis of the orbit solution. In this research, DSODS is demonstrated and validated at lunar orbit at an altitude of 100 km using actual Lunar Prospector tracking data. A set of a priori states are generated, and the robustness of DSODS to the a priori error is confirmed by the NASA planetary data system (PDS) orbit solutions. Furthermore, the accuracy of the orbit solutions is determined by solution comparison and overlap analysis as about tens of meters. Through these analyses, the ability of the DSODS to provide proper orbit solutions for the KPLO are proved.

• The Bulletin of The Korean Astronomical Society
• /
• v.42 no.2
• /
• pp.82.2-82.2
• /
• 2017

Korea Microlensing Telescope Network (KMTNet) is one of powerful tools for investigating primordial objects in the inner solar system in that it covers a large area of the sky ($2{\times}2$ degree2) with a high observational cadence. The Deep Ecliptic Patrol of the Southern sky (DEEP-South) survey has been scanning the southern sky using KMTNet for non-bulge time (45 full nights per year) [1] since 2015 for examining color, albedo, rotation, and shape of the solar system bodies. Since 2017 January, we have launched a new collaborative group between Korea Astronomy and Space Science Institute (KASI) and Seoul National University (SNU) with support from KASI to reinforce mutual collaboration among these institutes and further to enhance human resources development by utilizing the KMTNet/DEEP-South data. In particular, we focus on the detection of comets and asteroids spontaneously scanned in the DEEP-South for (1) investigating the secular changes in comet's activities and (2) analyzing precovery and recovery images of objects in the NASA's NEOWISE survey region. In this presentation, we will describe our scientific objectives and current status on using KMTNet data, which includes updating the accuracy of the world coordinate system (WCS) information, finding algorithm of solar system bodies in the image, and doing non-sidereal photometry.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.2
• /
• pp.365-380
• /
• 2022

The target detection algorithm based on supervised learning is the current mainstream algorithm for target detection. A high-quality dataset is the prerequisite for the target detection algorithm to obtain good detection performance. The larger the number and quality of the dataset, the stronger the generalization ability of the model, that is, the dataset determines the upper limit of the model learning. The convolutional neural network optimizes the network parameters in a strong supervision method. The error is calculated by comparing the predicted frame with the manually labeled real frame, and then the error is passed into the network for continuous optimization. Strongly supervised learning mainly relies on a large number of images as models for continuous learning, so the number and quality of images directly affect the results of learning. This paper proposes a dataset STAR-24K (meaning a dataset for Space TArget Recognition with more than 24,000 images) for detecting common targets in space. Since there is currently no publicly available dataset for space target detection, we extracted some pictures from a series of channels such as pictures and videos released by the official websites of NASA (National Aeronautics and Space Administration) and ESA (The European Space Agency) and expanded them to 24,451 pictures. We evaluate popular object detection algorithms to build a benchmark. Our STAR-24K dataset is publicly available at https://github.com/Zzz-zcy/STAR-24K.