Martin Wickler

TerraSAR-X Mission Planning System: Automated Command Generation for Spacecraft Operations

On June 15, 2007, TerraSAR-X was successfully launched from Baikonur, Kazakhstan. On board TerraSAR-X, a high-resolution X-band synthetic aperture radar (SAR) instrument is being operated as the primary payload. The user community requesting SAR products is composed of commercial and scientific partners as documented in a public-private-partnership agreement. The operations of the TerraSAR-X bus as well as payload operations are performed by the Mission Operations Segment (MOS). The Mission Planning System (MPS), which is a part of the MOS, has been designed to handle complex payload and standard bus operations in an automated manner. The purpose of this paper is to describe the concepts and the TerraSAR-X realization of the MPS.

Mission Planning System for the TET-1 OnOrbitVerification Mission

The TET-1 satellite was launched on July 22nd, 2012, to test and demonstrate the space readiness of new hardware components. Eleven experiments are running in space since then. The mission planning system (MPS) that provides the TET-1 satellite with its tele-command timelines during the OnOrbitVerification (OOV) phase is presented: Based on a strategic one-year experiment plan provided in advance by an external industry partner, MPS collects all relevant information necessary to build a sequence of flight procedures, called timeline, for a time range of roughly a week, on a day-by-day basis. In contrast to the TerraSAR-X/TanDEM-X MPS or the Incremental Planning System, where several software components convert incoming orders into commandable files, a slim set of tools was decided to be used for the TET-1 mission, combined in PINTA (Program for INteractive Timeline Analysis). Necessary data was imported using the plug-in mechanism of PINTA that uses interfaces to several partners. Having all information available, scheduling itself was done by running the planning algorithms provided by Plato, GSOCs generic library for modeling and solving planning problems. An assembly of various planning algorithms, individually configurable and referencing one another, creates the necessary timeline entries of flight procedures. Due to the high flexibility of the planning system it was possible to support various changes in the pre-planned onboard timeline on short notice. Additionally, an outlook on further extensions of the current MPS is given, that enables even more flexibility in terms of data acquisition and are relevant for the upcoming FireBIRD mission, which includes the TET-1 spacecraft after the OOV operations phase.

Onboard Planning and Scheduling Autonomy withinthe Scope of the FireBird Mission

For most low orbiting earth observation satellite missions, the timeline is generated on- ground and during dedicated uplink sessions the corresponding tele-commands are sent to the spacecraft. Bene�ts of this approach are easy maintainability of the complex planning software and quick response times to customer input. However this approach has two major drawbacks: On the one hand the spacecraft behavior is not completely predictable in terms of constraining resources, which means that even detailed modeling requires margins for the on-board resources within the on-ground scheduling algorithms. On the other hand, the reaction time to onboard detected events includes at least the two upcoming ground station contacts, since data downlink and evaluation, (re-)planning and tele-command uplink have to be awaited before the spacecraft can perform new activities. This paper describes the �nal design and use cases of VAMOS, an experiment of DLR/GSOC, which will be part of the FireBird mission. VAMOS consists of a combined onboard / on-ground planning system, which resolves the above mentioned drawbacks by supplying limited onboard autonomy to the satellite, retaining the bene�ts of a ground based planning system as far as possible.

The Incremental Planning System—GSOC's Next- Generation Mission Planning Framework

The paper at hand presents the new generic framework for automated planning and scheduling in future mission planning systems developed at GSOC (German Space Operations Center). It evolved from the experiences made in past and current projects and the evaluation of internal and external requirements for upcoming projects. In customary systems such as the one used within GSOC’s TerraSAR-X/TanDEM-X mission, succeeding planning runs to combine all collected input to a consistent, conflict-free command timeline take place at fix, dedicated points in time, e.g. twice a day. In contrast and as a main difference, with the new system each new input is processed immediately and so a consistent up-to-date timeline is maintained at all times. We show that this approach provides a set of important advantages and new possibilities for spacecraft commanding and user satisfaction. For example, uplink schedules can be flexibly modified due to short-term notifications, or up-to-date, extensive information about the planning state is always available, which means that conflicts can be seen before finally submitting a new request and, if applicable, can be resolved by selecting a suggested solution scenario. The presented system constitutes a generic tool suite which is scalable in performance critical areas, which is configurable to various mission scenarios and which defines a dedicated set of interfaces, specifying the functionality that remains to be implemented by each individual project. The declared goal is that all upcoming GSOC missions will benefit from using the Incremental Planning framework in terms of cost reduction, implementation duration and system robustness.

Clouds Handling for Planning of Optical Space Missions

Every space mission which uses optical band, e.g. ground-satellite/satellite-ground laser telecommunication, optical earth observation, on-ground optical space debris tracking system, is drastically affected by the clouds in the troposphere of the Earth. Mission planning group (MPS) of the German Space Operations Center (GSOC) is investigating the possibility to achieve the maximum performance of future optical space missions. Planning of an optical space mission should be fulfilled by involving the cloud coverage data. Short-term predicted future data are helpful for operational planning of an on-ground station and the relevant satellite. Long-term past data can be used to design a optimal network of on-ground stations and to assess the performance of the whole mission. Such data is distributed by DWD (Germany), ECMWF (UK), NOAA (USA), Weatheroffice (Canada) in the GRIB standard format. During the preliminary investigation data from ECMWF for the past period between 01.01.1992 and 31.12.1996 and some forecasted data for future periods from NOAA were used. Several methods of time-interpolation of the cloud coverage data were compared with and without using wind information and at least two criteria of on-ground stations network designing were applied. As a result a software tool was developed which takes GRIB files, orbit of the mission-satellite and some design restrictions as input and calculate an optimal list of on-ground stations with their coordinates and assessed performance. The performance is expressed in time during which a cloudless link can be established between an on-ground station and the satellite. The results of the work can be compared with some similar research, e.g. [1], [2]. [1] Kenta Ogawa et al., Usage of Cloud Climate Data in Operation Planning for Japanese Future Hyperspectral and Multispectral Senor: HISUI, GFZ Meeting, 23.09.2011 [2] C. Fuchs et al., Verification of Ground Station Diversity for Direct Optical TTC-downlinks from LEO Satellites by Means of an Experimental Laser-source, 5th ESA International Workshop on Tracking, Telemetry and Command Systems for Space Applications, 21 – 23 September 2010.

The TerraSAR-X Ground Segment: A Successful Story of Space Operations

On June 15th 2007 Germany’s first national remote sensing satellite, TerraSAR-X, was launched; on June 19th 2007 the first pictures were received and processed. TerraSAR-X is implemented in a public-private partnership between the German Aerospace Centre (DLR) and EADS Astrium GmbH, with a significant financial contribution from the industrial partner. This radar satellite supplies high-quality radar data for purposes of scientific observation of the earth for a period of at least five years. At the same time it is designed to satisfy the steadily growing demand of the private sector for remote sensing data in the commercial market. This paper will describe at first the development of the TerraSAR-X ground segment as well as the operations concept in the context of previous national SARrelated activities. Additionally the roles and responsibilities of the partners as well as the overall project organization are shown. The TerraSAR-X ground segment is located at DLR in Oberpfaffenhofen and consists of the Missions Operations Segment (MOS), the Payload Ground Segment (PGS) and the Instrument Operations and Calibration Segment (IOCS). The system design and the operations concept will be described with an emphasis on the mission operations segment as an essential part of the overall ground segment. Mission operations consist not only of operation of the satellite and its prime payload (the radarinstrument) but also of the secondary payload (e.g. a Laser Communication Terminal, LCT) as well. The contribution will then focus on the results and experiences of the LEOP and commissioning phase of TerraSAR-X mission operations and gives an overview about the actual mission status. Finally a brief outlook will be given on the activities to come.

ATHMoS: Automated Telemetry Health Monitoring System at GSOC using Outlier Detection and Supervised Machine Learning

Knowing which telemetry parameters are behaving accordingly and those which are behaving out of the ordinary is vital information for continued mission success. For a large amount of different parameters, it is not possible to monitor all of them manually. One of the simplest methods of monitoring the behavior of telemetry is the Out Of Limit (OOL) check, which monitors whether a value exceeds its upper or lower limit. A fundamental problem occurs when a telemetry parameter is showing signs of abnormal behavior; yet, the values are not extreme enough for the OOL-check to detect the problem. By the time the OOL threshold is reached, it could be too late for the operators to react. To solve this problem, the Automated Telemetry Health Monitoring System (ATHMoS) is in development at the German Space Operation Center (GSOC). At the heart of the framework is a novel algorithm for statistical outlier detection which makes use of the so-called Intrinsic Dimensionality (ID) of a data set. Using an ID measure as the core data mining technique allows us to not only run ATHMoS on a parameter by parameter basis, but also monitor and flag anomalies for multi-parameter interactions. By aggregating past telemetry data and employing these techniques, ATHMoS employs a supervised machine learning approach to construct three databases: Historic Nominal data, Recent Nominal data and past Anomaly data. Once new telemetry is received, the algorithm makes a distinction between nominal behaviour and new potentially dangerous behaviour; the latter of which is then flagged to mission engineers. ATHMoS continually learns to distinguish between new nominal behavior and true anomaly events throughout the mission lifetime. To this end, we present an overview of the algorithms ATHMoS uses as well an example where we successfully detected both previously unknown, and known anomalies for an ongoing mission at GSOC.

Optimization of positioning of ground stations for space optical missions

Every space mission which uses optical band, e.g. ground-satellite/satellite-ground laser telecommunication, optical earth observation, on-ground optical space debris tracking system, is drastically affected by the clouds in the troposphere of the Earth. Mission planning group of the German Space Operations Center (GSOC) is investigating the possibility of achieving the maximum performance of future optical space missions by involving cloud cover information (CI).

XTCE at GSOC - First Experiences Adopting a New Standard

On the level of data protocols and their usage we have come along a long path in the last decades having standardized the transport mechanisms, data protocols and packet layers; first missions are applying the packet utilization standard. But still the content of telemetry and command data is as varied as the missions and payloads themselves. Project by project one had to agree on a common “language”, i.e. an exchange format for the description of telemetry and command data-bases, which turned out more cumbersome than the eventual communication with the space segment. Given complete freedom when designing on-board software, developers would find a myriad of clever solutions, resulting in different “user interfaces” for satellites and even more possibilities on documenting these. Different operation entities, user centres, bus and payload controllers have to co-operate. They are equipped with their own systems, coming from different technical heritages and company cultures. However all project-participants are dealing with the same technical subject: a common spacecraft. It is obvious that transforming databases for no other purpose as to make it readable for the organization-owned software is a waste of time and resources. Following popular examples of today’s information technology, it was proposed to use a formal mark-up language could build a bridge between the diverse systems. For this purpose a formal language has been developed, which is XTCE. This evolving standard shall facilitate the task of describing the telemetry and command data and communicating it between operation entities and spacecraft manufacturers. This paper will report on the experiences of the first implementation of XTCE for the TM/TC database management at GSOC.

Numerical Analysis of Automated Anomaly Detection Algorithms for Satellite Telemetry

As technology evolves and the complexity of satellites and the amount of available telemetry increases, the manual inspection of thousands of parameters in detail per satellite becomes less and less manageable. While automated processes such as Out-Of-Limit (OOL) checks, which verify if a parameter exceeds an upper or lower threshold, exist, they come with the drawback of needing to be defined manually and often being very coarse to detect subtle changes in the telemetry. As this is a known problem, many space agencies are developing anomaly detection systems using machine learning methods. We found that the main difficulty in developing such an algorithm, as has been done for the Automated Telemetry Health Monitoring System (ATHMoS) at German Space Operations Center (GSOC), is minimizing the number of false positives while still detecting anomalies at a sufficiently high rate. Also, computational cost needs to be minimized since the detection algorithm needs to run at least once per day for all parameters. Considering these important constraints specific to automatic anomaly detection for satellite telemetry, we analyse several algorithms commonly used, namely the LOF and LoOP algorithms, as well as, in more detail, the novel algorithm developed at GSOC named Outlier Probability Via Intrinsic Dimension (OPVID) with regards to these constraints. To this extent, we will use both academic and custom benchmarks based on artificial data and historic satellite telemetry to highlight the difficulties as well as provide solutions for choosing the right algorithms and their parameters for the wanted results. In addition to the analysis of the different algorithms for these benchmarks with mostly predefined features used as the algorithm input, we also want to provide a compact analysis of different features unique to their use case for satellite telemetry as an input to the OPVID algorithm. The results can also be extrapolated for various other algorithms. In an operational use case, these features need to be generic enough to describe every available telemetry parameter and, at the same time, provide a context for the engineers as the automated system should complement the operations team. In the result, we will see that the selection of the features has a large effect on both the false positive and true positive rate and is one of the keys to designing an anomaly detection system for an operational use case.