Precise knowledge about the orbit state vectors and the associated uncertainties are crucial for reliable conjunction assessment and efficient mitigation of on-orbit collision risks through collision avoidance maneuvers by active satellites. The decision-making process heavily relies on the orbit accuracy and the uncertainty realism of the encountering objects, primarily space debris, and the maneuvering satellite itself. The process of determining an orbit creates an estimate of the orbit state and its associated state covariance matrix. This estimate serves as the initial value for predicting the orbit's uncertainty at the time of closest approach. To determine and numerically propagate realistic orbit errors, the uncertainties of the dynamic model must be taken into account. One method that fulfils this purpose is consider covariance propagation, provided that suitable consider parameters and their variances have been established. This paper presents a novel method for optimizing the consider covariance parameters from past mission data. Real orbit data from multiple low Earth orbit satellites controlled by the DLR, German Aerospace Center's German Space Operations Center are used to calibrate and evaluate different error models, including covariance parameters and other error terms. The results demonstrate improved uncertainty realism in orbit predictions and subsequent benefits for collision avoidance. Finally, this paper presents the required steps for integrating the new method into the existing collision avoidance system, along with initial results from conjunction assessment.

This paper proposes two innovative methods for detecting satellite manoeuvres by analysing a timeline of orbit elements. The first one makes use of a Linear Kalman Filter (LKF) to track the evolution of orbit elements and identify changes, while the second one refers to a particular manoeuvre strategy of geostationary satellites and detects in-plane orbit corrections by evaluating differences in mean longitude. Their detection performances are compared to those of already-existing techniques and tested, when applicable, for both Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) regimes. An optimization process is put in place to fairly confront different detection algorithms with distinct tuning parameters in order to ensure that every method is always providing its best detection performance. The defined loss function can also be used for a relative comparison of results, and this proves a better performance of the newly developed manoeuvre detection techniques.

In the last decades, the Earth-orbiting population of both active and non-active objects has grown significantly, leading to a substantial increase in number of possible in-orbit collisions. It is therefore crucial to monitor the orbit of space resident objects to assess in advance the threat of risky conjunctions. Within this framework, the 18th Space Defense Squadron (SDS) is consistently updating the orbit of thousands of tracked objects by processing observations of the U.S. Space Surveillance Network (SSN). The determined orbital data is continuously maintained in the Special Perturbation (SP) catalogue and used by the 19th SDS to issue close approach warnings to satellite operators around the globe in the form of Conjunction Data Messages (CDM). The Flight Dynamics (FD) group of the German Space Operation Centre (GSOC) receives on regular basis a subset of the SP catalogue data along with CDMs associated to the fleet of its controlled satellites. The SP ephemerides are in fact provided without any covariance information preventing any computation of the Probability of Collision (Pc). In GSOC FD we are implementing a service to link a series of synthetic orbital error covariance matrices to a given SP ephemeris by statistically analyzing historical CDMs of past events. More than 30 GB of past conjunction data are processed to extract state vector, covariance matrix and object size parameter of already encountered secondary objects. The orbital errors of these last are subsequently categorized and divided into orbital classes to decouple the high correlation the covariance has with respect to solar flux, object dimension, altitude of perigee, eccentricity and orbit inclination. The classification aims at collecting similar CDMs regarding the aforementioned dependencies, and approximates the predicted 1-sigma position errors in the orbital frame by optimal curve-fitting techniques. By evaluation of the curve fitting coefficients of a requested orbit class a covariance matrix can be generated for any prediction time in upcoming CDM refinements and other analyses. The work discusses the limiting cases of the classification approach, bringing possible solutions to the scenario of empty classes. An in-depth characterization of the parameters that affect the orbital errors is in fact performed to individualize the neighboring class that provides the closest and most meaningful covariance timeline. Successively, the effect of using synthetic covariance in a conjunction risk assessment is also explored, adapting the problem on real operations. Lastly, the entire data processing pipeline and how the described service fits into the GSOC Flight Dynamics System (FDS) framework is described.

For Space Situational Awareness, the German Aerospace Center (DLR) develops the software system "Backbone Catalogue of Relational Debris Information" (BACARDI), which allows for keeping track of resident space objects. BACARDI's key features are automated processing services which produce orbit information and products like collision warnings. We present how we applied new methods of software analytics to the BACARDI project. BACARDI is an example of a complex software system with large development effort carried out by a team of various specialists. Our goal is to design and implement an efficient software development process, balancing the explorative character of a research project and operational requirements (i.e. tailored from official standards in the aerospace domain). Therefore, we established a software development process for the project where we focus on software quality. We applied methods to structure, communicate, and utilize the diverse skills, knowledge, and experience in the team concisely and precisely. After one year of practical utilization, we analyzed the process based on the repository data. By analyzing these data, we assess and prove the effects of the introduced process on the development of a software, which is used in the aerospace domain.

Situation awareness of objects in the geostationary regime is of great interest for collision avoidance by active satellites as well as for scientific research on the space debris population and their evolution over time. As the number of satellite operators and researches in this field is large, it makes sense to set up a sensor network with multiple entities to combine all available sensor measurements for a comprehensive situational picture. This will allow for cost sharing and optimising observation strategies to gain as much information as possible about the desired objects. Therefore, the German Space Operation Center, GSOC, together with the Astronomical Institute of the University of Bern, AIUB, are setting up a global optical sensor network called SMARTnet™: the Small Aperture Robotic Telescope Network. The main objective is the free exchange of all information gathered, mainly in form of tracklet observations, within all partners involved. In this paper, the principles of SMARTnet™ are presented, how other entities can join the network and how tracklets are exchanged. The first two stations at Zimmerwald and Sutherland are introduced as well as possible future locations for additional telescope stations. Furthermore, results from the first two stations are shown. Additionally, a short insight is given in the data processing chain like object correlation, orbit determination, and cataloguing function.

In this paper, a scoring metric for space surveillance sensor observations is introduced. A scoring metric allows for direct comparison of data quantity and data quality, and makes transparent the effort made by different sensor operators. The concept might be applied to various sensor types like tracking and surveillance radar, active optical laser tracking, or passive optical telescopes as well as combinations of different measurement types. For each measurement type, a polynomial least squares fit is performed on the measurement values contained in the track. The track score is the average sum over the polynomial coefficients uncertainties and scaled by reference measurement accuracy.Based on the newly developed scoring metric, an accounting model and a rating model are introduced. Both models facilitate the exchange of observation data within a network of space surveillance sensors operators. In this paper, optical observations are taken as an example for analysis purposes, but both models can also be utilized for any other type of observations. The rating model has the capability to distinguish between network participants with major and minor data contribution to the network. The level of sanction on data reception is defined by the participants themselves enabling a high flexibility. The more elaborated accounting model translates the track score to credit points earned for data provision and spend for data reception. In this model, data reception is automatically limited for participants with low contribution to the network.The introduced method for observation scoring is first applied for transparent data exchange within the Small Aperture Robotic Telescope Network (SMARTnet). Therefore a detailed mathematical description is presented for line of sight measurements from optical telescopes, as well as numerical simulations for different network setups. (C) 2017 COSPAR. Published by Elsevier Ltd. All rights reserved.

In a joint project of the DLR Institute for Simulation and Software Technology and DLR Space Operations and Astronaut Training develop methods for space surveillance. The main objective of the project is to develop an orbital database for objects in Earth orbit – called the Backbone Catalogue of Relational Debris Information (BACARDI). The research topics are object identification form different sensor observations, orbit determination and orbit propagation including state vector and state uncertainty. Main applications of the orbital database will be close approach prediction for collision avoidance - further on re-entry prediction, detection of satellite manoeuvres and on-orbit fragmentations. In this paper an introduction is given to the space object catalogue BACARDI. All information imported, processed and exported is stored within a relational database. A self-developed middleware provides the functionality for distributed computations on a scalable network of hardware devices. The data processing chain consists of a multi-staged algorithm of observation correlation and subsequent orbit determination. Results are presented for single processing steps by example of optical telescope observations of satellites in MEO and GEO.

In order to guarantee save operation of satellites in the geostationary orbit, space object databases are created. Close encounters between uncontrollable and active objects must be detected and maneuvers must be planned to avoid future collisions. The geostationary orbit is typically monitored using ground-based optical telescopes. They are operated in a surveillance mode, i.e. the entire region is covered by the observation strategy to ensure a complete catalog. Due to limited resources, objects can only be observed for short durations. The resulting measurement arcs, called tracklets, do not provide enough information to determine the full state of the objects. When building up the database using no prior information, tracklets must be associated to each other to obtain object candidates. An efficient method was developed for this task, which optimally uses the information contained in the short sequences of angular measurements, i.e. the line-of-sights and their derivative. The resulting candidate solutions are either confirmed and refined with further observations or rejected. This work will examine whether it is feasible to build up a catalog using the developed algorithms and short tracklets as observations. A simulation of an optical sensor network is performed. Near-geostationary objects are extracted from publicly available catalogs. The developed method is then used to generate the catalog. The resulting object database is analyzed for accuracy and completeness. The study will outline the performance of such a system and identify deficiencies.

Any future space debris removal or on-orbit servicing mission faces the problem of the initial relative orbit determination of the servicing satellite to the non-cooperative target. In this work, we analyse the relative navigation accuracy that can be achieved in low Earth orbit, by using ground-based orbit determination from radar tracking measurements for the target, and classical GPS-based orbit determination for the servicing satellite. The analysis is based on the radar tracking measurements obtained from a 10 × 10 × 34 cm small object at an altitude of 635 km. The results show that the relative orbit can be determined with accuracy down to 2 m (RMS) in the semi-major axis, and down to 20 m (RMS) in both the radial and normal separations. From the results, we derive requirements on radar-tracking campaigns.

Debris populating the geostationary orbit is mainly observed by ground-based optical telescopes. Short measurement arcs, known as tracklets, provide line-of-sight information along with the associated rates of change. In view of their limited duration, individual tracklets cannot be used to determine a full set of orbital elements for the observed object. Multiple hypotheses filter methods have, therefore, been proposed to associate independent tracklets and to combine them for initial orbit determination. Using a traditional search grid for the admissible region, these methods become computationally intensive with an increasing number of initial hypotheses. As an alternative, this paper proposes a minimum search method to find the best matching orbit hypotheses. The effectiveness of the presented method is assessed using simulated measurements.

Independent of light and weather conditions radar systems provide observations of space debris in low and medium earth orbits. In general two operational principles can be distinguished - in survey mode all objects passing through the radar instrument’s field of view and above a certain size threshold are detected; subject to the tracking mode is the task to collect positional information on an already detected object. A recently proposed radar concept balances survey and tracking performance in a novel way by combining a mechanical steerable reflector antenna with digital beam-forming techniques. The utilization of multiple digital feed elements allows illumination of a larger survey zone, compared to the relative narrow pencil beam of conventional tracking radars. Signal processing from independent digital channels preserves a high antenna gain on the reception path. It is also a prerequisite for the implementation of an advanced Track While Scan operational mode. During design phase an optimal combination of surveillance and tracking functionality will have to be found. As the number of simultaneously observable space debris is directly linked to the angular extension of the survey zone plus the amount of transmitted/received power, it is straightforward to define system requirements to meet an envisaged survey performance. Moreover, it is less obvious to characterize tracking data that lead to reliable and accurate orbit determination results. Variables of influence are the type of involved measurement data, their temporal and geometrical distribution as well as systematic measurement errors, e.g. measurement bias and noise, or erroneous measurement correction and force modelling. The amount of error introduced by each variable can be quantized in a so called Consider Covariance Analysis. This paper presents the outcomes of this kind of orbit determination error analysis. Qualitative simulations for a small set of tracking scenarios serve as a starting point for the establishment of tracking requirements for a possible future space debris radar located at DLR ground station in Weilheim. The analyzed tracking scenarios are representative for three satellite missions currently operated at German Space Operation Centre (GSOC) and take into account the timeliness constrains inhered from a tracking request for refined orbit determination in case of a conjunction warning. Based on analysis findings the number of station passes, space debris can be tracked on, is investigated in more detail and an analytical formula is given for the optimization of minimum elevation angle.

The German Space Operations Center (GSOC) performs collision avoidance for 11 LEO and 2 GEO satellites. Risk detection and maneuver decisions strongly depend on the computed probability of collision that is driven by the anticipated orbit precision of chaser and target. While the orbits of operational satellite are well known this is usually not the case for space debris. Therefore, an improved collision assessment requires refined orbit determination of the chaser object. This paper describes the achievable orbit precision for a small object based on radar measurements. The Tracking and Imaging Radar (TIRA) of Fraunhofer FHR in Wachtberg, Germany, was used to track the Canadian nanosatellite CanX-2 over a period of five days. CanX-2 is a triple CubeSat of the size 10x10x34 cm carrying a dual frequency GPS receiver. A reference trajectory is established by precise orbit determination (POD) from GPS measurements. Radar tracking measurements and derived orbital information are evaluated by comparison against the reference orbit. Statistics of the orbit determination and orbit prediction precision using different radar measurement data arc lengths is presented leading to a better understanding of the prediction uncertainty of critical close approaches between an active satellite and a small object.