Near Real Time Satellite Event Detection and Characterization with Remote Photoacoustic Signatures

Active satellites frequently maneuver to mitigate conjunctions and maintain nominal mission orbits. With an ever-growing Resident Space Object (RSO) population, the need to detect and predict any changes in active RSO trajectories has become increasingly important. There is typically a lag on the order of hours to days from time of maneuver to unmodeled dynamic event detection depending on the magnitude of the delta-v. For uncooperative objects, this detection lag poses a threat to other satellites. Implementing an active photoacoustic signature change detection methodology to detect and predict unmodeled dynamic events would reduce the overall conjunction risk and provide a means for a near real time pulse of satellite events [1]. If photometric data is collected at a sufficient rate, any changes in outgoing photon flux due to satellite body vibrations caused by on-board events can be detected. The analysis of high-rate light curve data in the photometric, frequency, and photoacoustic domains can thus help characterize the event and provide mission specific intelligence. This research also investigates the use of signal processing methods, primarily cross-correlation, to improve the satellite body minimum displacement detection threshold in the presence of noise induced by the chaotic atmosphere. [1] Spurbeck, J., Jah, M., Kucharski, D., Bennet, J., Webb, J. “Satellite Characterization, Classification, and Operational Assessment Via the Exploitation of Remote Photoacoustic Signatures.” Advanced Maui Optical and Space Surveillance Technologies Conference, Maui, Hawaii, 2018

Space Environmentalism

With more than 500,000 objects floating in space and only about 2,000 functioning, space junk is growing exponentially. When these objects stop working, they drift aimlessly into the cosmos. These rogue bits of metal and space debris pose a danger to the technologies we rely on and to the future of space exploration. Dr. Moriba Jah examines what we can do to make space safe, secure, and sustainable in the long term.Environmental Science Institut

Inertial Measurements for Aero-assisted Navigation (IMAN)

IMAN is a Python tool that provides inertial sensor-based estimates of spacecraft trajectories within an atmospheric influence. It provides Kalman filter-derived spacecraft state estimates based upon data collected onboard, and is shown to perform at a level comparable to the conventional methods of spacecraft navigation in terms of accuracy and at a higher level with regard to the availability of results immediately after completion of an atmospheric drag pass

Synchronic Curation for Assessing Reuse and Integration Fitness of Multiple Data Collections

Data driven applications often require using data integrated from different, large, and continuously updated collections. Each of these collections may present gaps, overlapping data, have conflicting information, or complement each other. Thus, a curation need is to continuously assess if data from multiple collections are fit for integration and reuse. To assess different large data collections at the same time, we present the Synchronic Curation (SC) framework. SC involves processing steps to map the different collections to a unifying data model that represents research problems in a scientific area. The data model, which includes the collections' provenance and a data dictionary, is implemented in a graph database where collections are continuously ingested and can be queried. SC has a collection analysis and comparison module to track updates, and to identify gaps, changes, and irregularities within and across collections. Assessment results can be accessed interactively through a web-based interactive graph. In this paper we introduce SC as an interdisciplinary enterprise, and illustrate its capabilities through its implementation in ASTRIAGraph, a space sustainability knowledge system

ASTRIA Ontology: Open, Standards-based, Data-aggregated Representation of Space Objects

The necessity for standards-based ontologies for long-term sustainability of space operations and safety of increasing space flights has been well-established [6, 7]. Current ontologies, such as DARPA’s OrbitOutlook [5], are not publicly available, complicating efforts for their broad adoption. Most sensor data is siloed in proprietary databases [2] and provided only to authorized users, further complicating efforts to create a holistic view of resident space objects (RSOs) in order to enhance space situational awareness (SSA). The ASTRIA project is developing an open data model with the goal of aggregating data about RSOs, parts, space weather, and governing policies in order to provide a comprehensive awareness of space objects and events. The first step in this direction involves modeling RSOs. Our standards-based, graph data model adopts design and documentation best practices as well. The model expresses data using well-known general-purpose data modeling schemas (such as Dublin Core [1] and OAI-ORE [4]), and orbit representations (such as Keplerian elements and position-values), and controlled vocabularies (e.g. DISCOS classifications of space debris, orbital regimes, and fragmentation events [3]) expressed as Resource Description Framework (RDF) triples. Recognizing uncertainties in tracking as well as associating RSOs with known objects, our model supports name or track-based initiation, incremental specification, and uncertainty in association. De-siloing data is the first step toward enabling discovery regarding impact of the space environment and human based activity on space object behavior. We intend the ASTRIA ontology to support data-driven decision-making processes in order to make the space domain safe, secure, and sustainable

Mars Reconnaissance Orbiter Interplanetary Cruise Navigation

Carrying six science instruments and three engineering payloads, the Mars Reconnaissance Orbiter (MRO) is the first mission in a low Mars orbit to characterize the surface, subsurface, and atmospheric properties with unprecedented detail. After a seven-month interplanetary cruise, MRO arrived at Mars executing a 1.0 km/s Mars Orbit Insertion (MOI) maneuver. MRO achieved a 430 km periapsis altitude with the final orbit solution indicating that only 10 km was attributable to navigation prediction error. With the last interplanetary maneuver performed four months before MOI, this was a significant accomplishment. This paper describes the navigation analyses and results during the 210-day interplanetary cruise. As of August 2007 MRO has returned more than 18 Terabits of scientific data in support of the objectives set by the Mars Exploration Program (MEP). The robust and exceptional interplanetary navigation performance paved the way for a successful MRO mission

Mars Reconnaissance Orbiter Aerobraking Daily Operations and Collision Avoidance

The Mars Reconnaissance Orbiter reached Mars on March 10, 2006 and performed a Mars orbit insertion maneuver of 1 km/s to enter into a large elliptical orbit. Three weeks later, aerobraking operations began and lasted about five months. Aerobraking utilized the atmospheric drag to reduce the large elliptical orbit into a smaller, near circular orbit. At the time of MRO aerobraking, there were three other operational spacecraft orbiting Mars and the navigation team had to minimize the possibility of a collision. This paper describes the daily operations of the MRO navigation team during this time as well as the collision avoidance strategy development and implementation

Autonomous global sky surveillance with real-time robotic follow-up: Night Sky Awareness through Thinking Telescopes Technology

Abstract We discuss the development of prototypes for a global grid of advanced "thinking" sky sentinels and robotic followup telescopes that observe the full night sky to provide real-time monitoring of the night sky by autonomously recognizing anomalous behavior, selecting targets for detailed investigation, and making real-time, follow-up observations. The layered, fault-tolerant, network uses relatively inexpensive robotic EO sensors to provide persistent autonomous monitoring and real-time anomaly detection to enable rapid recognition and a swift response to transients as they emerge. This T3 global EO grid avoids the limitations imposed by geography and weather to provide persistent monitoring of the night sky