Ok-Chul Jung

A computational approach to reduce the revisit time using a genetic algorithm

The genetic algorithms (GAs) have been recently used in many design problems including an orbit design and trajectory optimization problems due to their global search capability and the robust characteristics. Furthermore, the GAs do not require convenient analytical representations of the problem any more. For this reason, the GAs can provide good solutions to the orbital dynamics design problem, especially a local coverage problem of Low Earth Orbit (LEO), which is difficult to formulate and handle the problem since an orbit propagation and local coverage analysis should be performed at the same time. This paper presents the possibility to using a genetic algorithm as a computational approach to finding the temporary target orbit in order to reduce the average revisit time of current mission orbit over a particular target site during the specified days. Through a comprehensive simulation study, the possibility of using a genetic algorithm to apply this kind of a temporary reconnaissance mission using a single LEO spacecraft is successfully demonstrated.

An Optimal Satellite Antenna Profile Using Reinforcement Learning

This paper addresses a detailed procedure to generate an optimal satellite antenna profile. The goal of antenna profile is to provide a sequence of commands for antenna movements such that the antenna directs as many ground station as possible under some constraints. The main task in generating the antenna profile is to schedule the antenna movements taking account of satellite orbit and attitude at all time points, given a mission trajectory. To generate the antenna profile, it is necessary to transform the direction of antenna from the antenna body frame to the satellite body frame and from the satellite body frame to the earth-centered fixed frame. For an optimal tracking of ground station, we generate a maneuvering sequence of azimuth and elevation angles of the antenna considering the projected beamwidth of the antenna on the ground, the off-pointing boundary, and the pointing errors. An optimal maneuvering sequence is generated by reinforcement learning (RL), which is an optimization search algorithm based on penalties and rewards obtained iteratively as episode increases. Through numerical simulations and with actual satellite data, the effectiveness of using RL is illustrated.

Genetic Design of Target Orbits for a Temporary Reconnaissance Mission

R ECENTLY, there has been increasing demand for highresolution geospatial information from space for civilian, as well as military users. However, there are obstacles to realizing a constellation of commercial high-resolution satellites over Earth’s entire surface remains subject to restrictions; these obstacles are primarily caused by the development cost and such an operation itself. For this reason, a sparse coverage constellation focusing on discontinuous coverage over a local area (or target) of interest could be an alternative. Several studies addressing the sparse coverage constellation design problems have been conducted by Lang [1], Crossley and Williams [2], and Williams et al. [3]. Ferringer and Spencer [4] designed a sparse constellation to resolve the conflict between revisit time and resolution. Meanwhile, a new constellation design proposed by Schiff and Mailhe [5] was applied to use resources from previously or soon-to-be launched satellites. Recently, a natural orbit whereby all sites are visited within a time frame without maneuvering was introduced by Abdelkhalik and Mortari [6]. However, very little of the work offered appropriate solutions for a “temporary reconnaissance mission” involving a few low-Earthorbit (LEO) satellites over a particular target site during a specified time. This is because each LEO satellite is currently in its own orbit with a limited fuel budget for orbital maneuvering and the sensor characteristics of each satellite are not identical. In this Note, the authors propose a new approach to find the target orbits of each satellite to establish a temporary reconnaissance constellation mission to minimize the average revisit time (ART) while satisfying the constraint on fuel limit. To achieve this goal, we employed a genetic algorithm (GA) to handle the discontinuity and nondifferentiability of the objective function for the given problem. The performance of the GA tends to be problem-dependent. Thus, preliminary efforts to improve the fitness function in dealing with a GA value are also presented.

Satellite antenna control: Design and performance validation under given TPF

This paper provides a control algorithm for satellite antenna tracking. An adaptive control scheme is developed to achieve asymptotical convergence of antenna azimuth and elevation angles to desired ones by compensating for unknown disturbances. The performance of developed control scheme is analyzed in terms of satellite tracking profile (TPF), which is uploaded to satellite from ground station for pointing the satellite antenna. Specifically, it will be discussed whether the results of the control scheme are satisfactory in achieving the desired azimuth and elevation angles under given sampling period and control voltage.

Orbit Analysis for KOMPSAT-2 During LEOP and Mission Lifetime

In this paper, results on the orbit analysis for the KOMPSAT-2 satellite using a real orbit data during the LEOP and normal mission lifetime are presented. In particular, the preparation and performance of an orbit operations during the LEOP is emphasized and the effects of space environments (i.e., Solar activity) on orbit evolutions are investigated comparing to those of the KOMPSAT-1 satellite. The summarized results in this paper would be an important reference to improve the stability and effectiveness of satellite operations during the LEOP and normal mission lifetime in case of LEO satellites such as successors of KOMPSAT-2 (i.e., KOMPSAT-3, KOMPSAT-3A, KOMPSAT-5).

Visibility conflict resolution for multiple antennae and multi-satellites via genetic algorithm

Satellite mission control systems typically are operated by scheduling missions to the visibility between ground stations and satellites. The communication for the mission is achieved by interacting with satellite visibility and ground station support. Specifically, the satellite forms a cone-type visibility passing over a ground station, and the antennas of ground stations support the satellite. When two or more satellites pass by at the same time or consecutively, the satellites may generate a visibility conflict. As the number of satellites increases, solving visibility conflict becomes important issue. In this study, we propose a visibility conflict resolution algorithm of multi-satellites by using a genetic algorithm (GA). The problem is converted to scheduling optimization modeling. The visibility of satellites and the supports of antennas are considered as tasks and resources individually. The visibility of satellites is allocated to the total support time of antennas as much as possible for users to obtain the maximum benefit. We focus on a genetic algorithm approach because the problem is complex and not defined explicitly. The genetic algorithm can be applied to such a complex model since it only needs an objective function and can approach a global optimum. However, the mathematical proof of global optimality for the genetic algorithm is very challenging. Therefore, we apply a greedy algorithm and show that our genetic approach is reasonable by comparing with the performance of greedy algorithm application.

Multi-satellite control system architecture and mission scheduling optimization

In this paper we propose an architecture for multi-satellite control systems and an optimization procedure for mission operations. The proposed multi-satellite control system architecture is obtained by structuring existing mission control systems into three organically connected components. : (1) the ground station responsible for the mechanical parts, (2) the users responsible for the overall management, and (3) the mission control element responsible for the overall scheduling and software operation. In addition, we present a mission scheduling optimization procedure involving numerous satellites and ground stations. The algorithm assumes the scheduling period time, the value and allocation time of the guidance parameter file for attitude control, and the mission value and image downlink allocation time. The visibility time between a ground station and satellites is calculated by simulation using the satellite tool kit. Hence, the scheduling problems can be treated as knapsack problems where the visibility time is analogous to the knapsack and the missions are considered to be the items. To solve the multi-mission scheduling problem, we applied the dynamic programming and greedy algorithms. We compared and analyzed the two algorithms.

Profile Optimization of Satellite Antenna for Angular Jerk Minimization

This paper presents the optimization of the satellite antenna profile (SAP) to minimize the angular jerk which is the time derivative of the angular acceleration. The method of moving asymptotes (MMA), which is a gradient-based optimization algorithm, is employed to solve the optimization problem. The sequential angle of the SAP is defined as the design variable for the optimization. The off-pointing margin angle, which is the maximum allowable range of the satellite antenna rotation for the communication with the ground station, is used for the side limits of the design variable. Three constraint sets with one objective function are formulated as the optimization problem. The objective function is the total sum of the squared angular jerk. The first set of constraints is the angular velocity and acceleration, the second is the angular jerk alone, and the third is the angular velocity, acceleration, and jerk. In numerical examples, two real SAPs are used for implementing the proposed optimization algorithm. The optimization results show the effectiveness of the proposed algorithm with great reduction of the angular jerk. The objective function and the computation time for the three sets of the optimization problems are compared and discussed.

Ground Antenna Scheduling Algorithm for Multi-Satellite Tracking

In this paper, we address the optimal ground antenna scheduling problem for multiple satellites when multiple satellites have visibility conflicts at a ground station. Visibility conflict occurs when multiple satellites have either overlapping visibilities at a ground station or difference with time of loss of signal (LOS) of a satellite and time of acquisition of signal (AOS) of another satellite is less than reconfiguration time of ground station. Each satellite has a priority value that is a weight function with various factors. Multi-antenna scheduling (MAS) algorithm 1 and Multi-antenna scheduling (MAS) algorithm 2 are proposed to find the optimal schedule of multi-antenna at a ground station using pre-assigned priority values of satellites. We use the depth first search (DFS) method to search the optimal schedule in MAS algorithm 1 and MAS algorithm 2. Through the simulations, we confirm the efficiency of these algorithms by comparing with greedy algorithm.Copyright

KOMPSAT-2 Orbit Determination Status Report

Korea-MultiPurpose-SATellite-2 (KOMPSAT-2) has been successfully operated by Mission Control Element (MCE) since the launch of July 28, 2006. The spacecraft was built by Korea Aerospace Research Institute (KARI) and the MCE system was developed by Electronics and Telecommunications Research Institute (ETRI). This paper presents status of the operational and precise KOMPSAT-2 Orbit Determination (OD) in Mission Analysis and Planning System (MAPS), one of MCE subsystems. The KOMPSAT-2 is the first satellite to perform Precise Orbit Determination (POD) using single frequency GPS data to process high resolution image data obtained from Low Earth Orbiter (LEO) satellite in Korea. Operational Orbit Determination (OOD) using GPS navigation solution data should satisfy 10 m Root-Sum-Square (RSS) in one sigma and Precise Orbit Determination (POD) is required to fulfill 1 m RSS in one sigma. In order to demonstrate the accuracy of the KOMPSAT-2 OD, OOD was compared with POD and POD was tested using orbit overlapping solution. The orbit accuracy of OOD and POD results met the given requirement, within 5-6 m RSS to the reference data and 60-75 cm RSS in orbit overlapping solution, respectively.