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As described in the previous paper (Park et al. 2013), the detector subsystem of optical wide-field patrol (OWL) provides many observational data points of a single artificial satellite or space debris in the form of small streaks, using a chopper system and a time tagger. The position and the corresponding time data are matched assuming that the length of a streak on the CCD frame is proportional to the time duration of the exposure during which the chopper blades do not obscure the CCD window. In the previous study, however, the length was measured using the diagonal of the rectangle of the image area containing the streak; the results were quite ambiguous and inaccurate, allowing possible matching error of positions and time data. Furthermore, because only one (position, time) data point is created from one streak, the efficiency of the observation decreases. To define the length of a streak correctly, it is important to locate the endpoints of a streak. In this paper, a method using a differential convolution mask pattern is tested. This method can be used to obtain the positions where the pixel values are changed sharply. These endpoints can be regarded as directly detected positional data, and the number of data points is doubled by this result.

The correlation between meteorological data collected at the optical wide-field patrol network (OWL-Net) Station No. 1 and the seeing of satellite optical observation data was analyzed. Meteorological data and satellite optical observation data from June 2014 to November 2015 were analyzed. The analyzed meteorological data were the outdoor air temperature, relative humidity, wind speed, and cloud index data, and the analyzed satellite optical observation data were the seeing full-width at half-maximum (FWHM) data. The annual meteorological pattern for Mongolia was analyzed by collecting meteorological data over four seasons, with data collection beginning after the installation and initial set-up of the OWL-Net Station No. 1 in Mongolia. A comparison of the meteorological data and the seeing of the satellite optical observation data showed that the seeing degrades as the wind strength increases and as the cloud cover decreases. This finding is explained by the bias effect, which is caused by the fact that the number of images taken on the less cloudy days was relatively small. The seeing FWHM showed no clear correlation with either temperature or relative humidity.

Inactive space objects are usually rotating and tumbling as a result of internal or external forces. KOREASAT 1 has been inactive since 2005, and its drift trajectory has been monitored with the optical wide-field patrol network (OWL-Net). However, a quantitative analysis of KOREASAT 1 in regard to the attitude evolution has never been performed. Here, two optical tracking systems were used to acquire raw measurements to analyze the rotation period of two inactive satellites. During the optical campaign in 2013, KOREASAT 1 was observed by a 0.6 m class optical telescope operated by the Korea Astronomy and Space Science Institute (KASI). The rotation period of KOREASAT 1 was analyzed with the light curves from the photometry results. The rotation periods of the low Earth orbit (LEO) satellite ASTRO-H after break-up were detected by OWL-Net on April 7, 2016. We analyzed the magnitude variation of each satellite by differential photometry and made comparisons with the star catalog. The illumination effect caused by the phase angle between the Sun and the target satellite was corrected with the system tool kit (STK) and two line element (TLE) technique. Finally, we determined the rotation period of two inactive satellites on LEO and geostationary Earth orbit (GEO) with light curves from the photometry. The main rotation periods were determined to be 5.2 sec for ASTRO-H and 74 sec for KOREASAT 1.

As a governmentally approved domestic entity for Space Situational Awareness, Korea Astronomy and Space Science Institute (KASI) is developing and operating an optical telescopes system, Optical Wide-field PatroL (OWL) Network. During the test phase of this system, it is necessary to determine the range of brightness of the observable satellites. We have defined standard magnitude for Low Earth Orbit (LEO) satellites to calibrate their luminosity in terms of standard parameters such as distance, phase angle, and angular rate. In this work, we report the optical brightness range of five LEO Satellites using OWL-Net.

Several optical monitoring strategies by a ground-based telescope to protect a Geostationary Earth Orbit (GEO) satellite from collisions with close approaching objects were investigated. Geostationary Transfer Orbit (GTO) objects, Inclined GeoSynchronous Orbit (IGSO) objects, and drifted GEO objects forced by natural perturbations are hazardous to operational GEO satellites regarding issues related to close approaches. The status of these objects was analyzed on the basis of their orbital characteristics in Two-Line Element (TLE) data from the Joint Space Operation Center (JSpOC). We confirmed the conjunction probability with all catalogued objects for the domestic operational GEO satellite, Communication, Ocean and Meteorological Satellite (COMS) using the Conjunction Analysis Tools by Analytical Graphics, Inc (AGI). The longitudinal drift rates of GeoSynchronous Orbit (GSO) objects were calculated, with an analytic method and they were confirmed using the Systems Tool Kit by AGI. The required monitoring area was determined from the expected drift duration and inclination of the simulated target. The optical monitoring strategy for the target area was analyzed through the orbit determination accuracy. For this purpose, the close approach of Russian satellite Raduga 1-7 to Korean COMS in 2011 was selected.

By using the Optical Wide-field Patrol (OWL) network developed by the Korea Astronomy and Space Science Institute (KASI) we generated the right ascension and declination angle data from optical observation of Low Earth Orbit (LEO) satellites. We performed an analysis to verify the optimum number of observations needed per arc for successful estimation of orbit. The currently functioning OWL observatories are located in Daejeon (South Korea), Songino (Mongolia), and Oukaïmeden (Morocco). The Daejeon Observatory is functioning as a test bed. In this study, the observed targets were Gravity Probe B, COSMOS 1455, COSMOS 1726, COSMOS 2428, SEASAT 1, ATV-5, and CryoSat-2 (all in LEO). These satellites were observed from the test bed and the Songino Observatory of the OWL network during 21 nights in 2014 and 2015. After we estimated the orbit from systematically selected sets of observation points (20, 50, 100, and 150) for each pass, we compared the difference between the orbit estimates for each case, and the Two Line Element set (TLE) from the Joint Space Operation Center (JSpOC). Then, we determined the average of the difference and selected the optimal observation points by comparing the average values.

A strategy is needed for a regional survey of geosynchronous orbits (GSOs) to monitor known space objects and detect uncataloged space objects. On the basis of the Inter-Agency Debris Committee’s recommendation regarding the protected region of geosynchronous Earth orbit (GEO), target satellites with perigee and apogee of GEO ± 200 km and various inclinations are selected for analysis. The status of the GSO region was analyzed using the satellite distribution based on the orbital characteristics in publicly available two-line element data. Natural perturbation effects cause inactive satellites to drift to two stable longitudinal points. Active satellites usually maintain the designed positions as a result of regular or irregular maneuver operations against their natural drift. To analyze the detection rate of a single optical telescope, 152 out of 412 active satellites and 135 out of 288 inactive satellites in the GSO region were selected on the basis of their visibility at the observation site in Daejeon, Korea. By using various vertical view ranges and various numbers of observations of the GSO region, the detection efficiencies were analyzed for a single night, and the numbers of follow-up observations were determined. The orbital estimation accuracies were also checked using the arc length and number of observed data points to maintain the GSO satellite catalog.

We estimated the orbit of the Communication, Ocean and Meteorological Satellite (COMS), a Geostationary Earth Orbit (GEO) satellite, through data from actual optical observations using telescopes at the Sobaeksan Optical Astronomy Observatory (SOAO) of the Korea Astronomy and Space Science Institute (KASI), Optical Wide field Patrol (OWL) at KASI, and the Chungbuk National University Observatory (CNUO) from August 1, 2014, to January 13, 2015. The astrometric data of the satellite were extracted from the World Coordinate System (WCS) in the obtained images, and geometrically distorted errors were corrected. To handle the optically observed data, corrections were made for the observation time, light-travel time delay, shutter speed delay, and aberration. For final product, the sequential filter within the Orbit Determination Tool Kit (ODTK) was used for orbit estimation based on the results of optical observation. In addition, a comparative analysis was conducted between the precise orbit from the ephemeris of the COMS maintained by the satellite operator and the results of orbit estimation using optical observation. The orbits estimated in simulation agree with those estimated with actual optical observation data. The error in the results using optical observation data decreased with increasing number of observatories. Our results are useful for optimizing observation data for orbit estimation.

An algorithm to automatically extract coordinate and time information from optical observation data of geostationary orbit satellites (GEO satellites) or geosynchronous orbit satellites (GOS satellites) is developed. The optical wide-field patrol system is capable of automatic observation using a pre-arranged schedule. Therefore, if this type of automatic analysis algorithm is available, daily unmanned monitoring of GEO satellites can be possible. For data acquisition for development, the COMS1 satellite was observed with 1-s exposure time and 1-m interval. The images were grouped and processed in terms of “action”, and each action was composed of six or nine successive images. First, a reference image with the best quality in one action was selected. Next, the rest of the images in the action were geometrically transformed to fit in the horizontal coordinate system (expressed in azimuthal angle and elevation) of the reference image. Then, these images were median-combined to retain only the possible non-moving GEO candidates. By reverting the coordinate transformation of the positions of these GEO satellite candidates, the final coordinates could be calculated.

The detector subsystem of the Optical Wide-field Patrol (OWL) network efficiently acquires the position and time information of moving objects such as artificial satellites through its chopper system, which consists of 4 blades in front of the CCD camera. Using this system, it is possible to get more position data with the same exposure time by changing the streaks of the moving objects into many pieces with the fast rotating blades during sidereal tracking. At the same time, the time data from the rotating chopper can be acquired by the time tagger connected to the photo diode. To analyze the orbits of the targets detected in the image data of such a system, a sequential procedure of determining the positions of separated streak lines was developed that involved calculating the World Coordinate System (WCS) solution to transform the positions into equatorial coordinate systems, and finally combining the time log records from the time tagger with the transformed position data. We introduce this procedure and the preliminary results of the application of this procedure to the test observation images.

We developed and compared two automatic algorithms for moving object detections in the YSTAR-NEOPAT sky survey program. One method, called starlist comparison method, is to identify moving object candidates by comparing the photometry data tables from successive images. Another method, called image subtraction method, is to identify the candidates by subtracting one image from another which isolates sources moving against background stars. The efficiency and accuracy of these algorithms have been tested using actual survey data from the YSTAR-NEOPAT telescope system. For the detected candidates, we performed eyeball inspection of animated images to confirm validity of asteroid detections. Main conclusions include followings. First, the optical distortion in the YSTAR-NEOPAT wide-field images can be properly corrected by comparison with USNO-B1.0 catalog and the astrometric accuracy can be preserved at around 1.5 arcsec. Secondly, image subtraction provides more robust and accurate detection of moving objects. For two different thresholds of 2.0 and 4.0σ, image subtraction method uncovered 34 and 12 candidates and most of them are confirmed to be real. Starlist comparison method detected many more candidates, 60 and 6 for each threshold level, but nearly half of them turned out to be false detections.