## 6 Space Situational Awareness  
Recently, the existing skills are being extended for the field of Space Situational Awareness (SSA). The focus of the research in the field of Space Surveillance and Tracking (SST), i.e., the awareness of current Resident Space Object (RSO) environment around Earth, which includes all space debris objects, is to optimize SST systems to a point where awareness is as \"live\" and accurate as possible. This is leading to the necessity of building capabilities to continuously survey and track RSOs. This is done by an orchestration of sensors, sensor-near data processing and data centers with orbit determination, cataloging and sensor tasking capabilities. The Institute is developing a simulator for such an SST system being built from specialized software tools (cf. Figure 9) [38]. The MWG tool produces artificial radar sensor measurements in the form of sets comprised of, among others, azimuth, elevation and range values. These sets are called tracklets and are coordinated by the PROCOR tool for further processing. PROCOR assigns the tracklets to instances of another tool called SMART to derive orbit data from them. The orbit data is saved in one of multiple maintained object data catalogs. The catalog data is used for evaluation purposes, such as visualization by a tool named DOCTOR and statistical analyses by a tool named CAT. In order to keep the stored orbit data fresh, the CAMP tool predicts where and when objects can be seen by sensors (\"Passes\"). These passes can be used by sensors or artificial measurement generators like MWG to rediscover the objects and produce new measurements. In particular, the existence of a validated population including the non-trackable objects plays an important role. It is about knowing in which orbits a high number of objects can be expected. This knowledge is a necessary prerequisite for being able to optimally design observation and tracking systems. In addition, it is possible to provide the basis for the effectiveness with which orbit determination of the observed objects is possible. An important spin-off research field based on the modeling skills is the orbit determination. With knowledge of the space debris environment, simulated detections can be provided to test algorithms that can be used to simulate the orbit determination of a large number of objects. Based on these competences, the Institute can simulate detections for selected sensors. It must now be decided whether the quality of the detection is sufficient to carry out a subsequent determination of the orbit. Above all, the duration of the observation and the possible repetition of the detection of the same object play an important role. It also simulates whether enough information has been collected to create an initial orbit. In the field of orbit determination, extensive investigations are currently underway. Several algorithms for initial orbit determination and orbit improvement may be utilized. The SST system is augmented by a sophisticated radar measurement generator to produce artificial measurements. The state of the overall system is that it implements the whole data flow from sensors and pre-existing RSO catalogs back to the tasking of sensors. The system is used to evaluate the performance of a given SST system configuration. Another area of research focuses on the oncoming requirements for SST systems connected to networks of sensor networks, and a technology named Data Stream Management Systems (DSMS) is researched in the SST context. The concept of DSMS arose from conventional database management systems being able to respond to technological advancements. An ever-increasing amount of relatively small devices is capable of producing sensor data and distributing it over networks. The conventional approach to save all raw data and to process it in batches regularly can often not keep up with the demand to get a  
Figure 9: Top-level data flow of the SST System Simulator. near-real-time view on what is happening.