Junzi Sun

Genetic algorithms for satellite scheduling problems

Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA European Space Agency and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we present some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present computational results obtained with Genetic Algorithms using the STK simulation toolkit.

Evaluation of Genetic Algorithms for Single Ground Station Scheduling Problem

Ground station scheduling problem arises in spacecraft operations and aims to allocate ground stations to spacecraft to make possible the communication between operations teams and spacecraft systems. The problem belongs to the family of satellite scheduling for the specific case of mapping communications to ground stations. Ground stations are terrestrial terminals designed for extra-planetary communications with spacecrafts. Spacecrafts are extra-planetary crafts including satellites, space stations, etc. The ground station scheduling problem is a highly constraint problem, among which, the most important is computing timing of spacecrafts communications with the ground stations. The problem can be seen as a time-window scheduling problem given that spacecrafts have their access window and visibility windows--visible time of a spacecraft to a ground station--are to be found avoiding the visibility clash among spacecrafts. The problem is indeed intractable and therefore it is unlikely to be solved in polynomial time to optimality. In this paper we evaluate the effectiveness of Genetic Algorithms (GAs) for near-optimally solving the problem. A data simulation model is used for the experimental study in order to realistically capture features of real instances and evaluate GAs for different scenarios. Computational results are given for the case of a single ground station. Through the experimental evaluation we could identify a set of parameter values that yielded the best performance of the GAs.

A Genetic Algorithm for Ground Station Scheduling

In this paper we address the resolution of the Ground Station Scheduling problem by Genetic Algorithms. Ground Stations (GS) are required during spacecraft (S/C) operations to provide communications links between operations teams and the S/C systems. Basic communication types, such as telemetry, tele-command and tracking, are all supported by the same satellite and GS systems. The allocation of ground resources to S/C is a highly constrained problem and traditionally has been conducted manually: stations are selected for communication support with certain S/C for certain periods. The manual approach has clear limitations and new modern scheduling approaches are needed to tackle with the complexity of the problem and produce optimal solutions to scheduling operations. We propose the use of Genetic Algorithm, a well-known family of population-based methods, for solving the problem. A series of genetic operators have been designed to find the configuration that outputs the best GA solution for the problem. The proposed GA has been implemented in Matlab and experimentally studied on a set of randomly generated instances. The results of the study showed the effectiveness of the proposed GA algorithm.

Mission Operations Scheduling: Complexity and Resolution Methods

Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA (European Space Agency) and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we survey some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present some computational results for the case of the multi-ground stations scheduling obtained with Genetic Algorithms using the STK simulation toolkit.

Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model

Wind and temperature data are important parameters in aircraft performance studies. The lack of accurate measurements of these parameters forces researchers to rely on numerical weather prediction models, which are often filtered for a larger area with decreased local accuracy. Aircraft, however, also transmit information related to weather conditions, in response to interrogation by air traffic controller surveillance radars. Although not intended for this purpose, aircraft surveillance data contains information that can be used for weather models. This paper presents a method that can be used to reconstruct a weather field from surveillance data that can be received with a simple 1090 MHz receiver. Throughout the paper, we answer two main research questions: how to accurately infer wind and temperature from aircraft surveillance data, and how to reconstruct a real-time weather grid efficiently. We consider aircraft as moving sensors that measure wind and temperature conditions indirectly at different locations and flight levels. To address the first question, aircraft barometric altitude, ground velocity, and airspeed are decoded from down-linked surveillance data. Then, temperature and wind observations are computed based on aeronautical speed conversion equations. To address the second question, we propose a novel Meteo-Particle (MP) model for constructing the wind and temperature fields. Short-term local prediction is also possible by employing a predictor layer. Using an unseen observation test dataset, we are able to validate that the mean absolute errors of inferred wind and temperature using MP model are 67% and 26% less than using the interpolated model based on GFS reanalysis data.

Flight Extraction and Phase Identification for Large Automatic Dependent Surveillance–Broadcast Datasets

AUTOMATIC dependent surveillance–broadcast (ADS-B) [1,2] is widely implemented in modern commercial aircraft and will become mandatory equipment in 2020. Flight state information such as position, velocity, and vertical rate are broadcast by tens of thousands of aircraft around the world constantly using onboard ADS-B transponders. These data are identified by a 24-bit International Civil Aviation Organization (ICAO) address, are unencrypted, and can be received and decoded with simple ground station set-ups. This large amount of open data brings a huge potential for ATM research. Most studies that rely on aircraft flight data (historical or real-time) require knowledge on the flight phase of each aircraft at a given time [3–7]. However, when dealing with large datasets such as from ADS-B, which can contain many tens of thousands of flights, exceptions to deterministic definitions of flight phases are inevitable, due to large variances in climb rate, altitude, velocity, or a combination of these. In this case, instead of using deterministic logic to process and extract flight data based on flight conventions, robust and versatile identification algorithms are required. In this paper, a twofold method is proposed and tested: 1) a machine learning clustering step that can handle large amounts of scattered ADS-B data to extract continuous flights, and 2) a flight phase identification step that can segment flight data of any type of aircraft and trajectory by different flight phases.