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

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 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

Probabilistic Initial Orbit Determination using Radar Returns

The most complete description of the state of a system at any time is given by knowledge of the probability density function, which describes the locus of possible states conditioned on any available measurement information. When employing radar returns, an admissible region approach provides a physics-based region of the right-ascension rate/declination rate space of possible Earth-bound orbit solutions. This work develops a method that employs a probabilistic interpretation of the admissible region and approximates the admissible region by a Gaussian mixture to formulate an initial orbit determination solution

Collision Probability with Gaussian Mixture Orbit Uncertainty

An extension for computing the probability of collision between two space objects was introduced when the uncertainties of the space objects are represented by Gaussian mixture distributions. Monte Carlo simulations have been used to compute the collision probability. The first method is based on the volume swept out by the hardball, whose dynamics relative to the other object are governed by the relative motion model. For short-term encounters with linear relative motion, the swept-out volume is approximately a cylinder. The second method, also known as the direct method, is based on the influx of the relative position probability distribution into the hardball which is assumed to be stationary. In each of these cases, the proposed method can be used to obtain the probability of collision between two space objects

Entropy-Based Approach for Uncertainty Propagation of Nonlinear Dynamical Systems

Uncertainty propagation of dynamical systems is a common need across many domains and disciplines. In nonlinear settings, the extended Kalman filter is the de facto standard propagation tool. Recently, a new class of propagation methods called sigma-point Kalman filters was introduced, which eliminated the need for explicit computation of tangent linear matrices. It has been shown in numerous cases that the actual uncertainty of a dynamical system cannot be accurately described by a Gaussian probability density function. This has motivated work in applying the Gaussian mixture model approach to better approximate the non-Gaussian probability density function. A limitation to existing approaches is that the number of Gaussian components of the Gaussian mixture model is fixed throughout the propagation of uncertainty. This limitation has made previous work ill-suited for nonstationary probability density functions either due to inaccurate representation of the probability density function or computational burden given a large number of Gaussian components that may not be needed. This work examines an improved method implementing a Gaussian mixture model that is adapted online via splitting of the Gaussian mixture model components triggered by an entropy-based detection of nonlinearity during the probability density function evolution. In doing so, the Gaussian mixture model approximation adaptively includes additional components as nonlinearity is encountered and can therefore be used to more accurately approximate the probability density function. This paper introduces this strategy, called adaptive entropy-based Gaussian-mixture information synthesis. The adaptive entropy-based Gaussian-mixture information synthesis method is demonstrated for its ability to accurately perform inference on two cases of uncertain orbital dynamical systems. The impact of this work for orbital dynamical systems is that the improved representation of the uncertainty of the space object can then be used more consistently for identification and tracking

Mars Aerobraking Spacecraft State Estimation by Processing Inertial Measurement Unit Data

Aerobraking is an efficient technique for orbit adjustment of planetary spacecraft, such as Magellan (Venus), Mars Global Surveyor, and Mars Odyssey. Determination of the vehicle state during the aerobraking phase has conventionally been performed using only radiometric tracking data prior to and following the atmospheric drag pass. This approach is sufficiently accurate and timely to meet current mission operational requirements; however, it is expensive in terms of ground support and leads to delayed results because ofthe need for post-drag pass data. This research presents a new approach to estimation of the vehicle state during the atmospheric pass that sequentially incorporates observations from an Inertial Measurement Unit (IMU) and models of the vehicle and environment. The approach, called Inertial Measurements for Aerobraking Navigation (IMAN), is shown to perform at a level comparable to the conventional methods in terms of navigation accuracy and superior to them in terms of availability of the results immediately after completion ofthe pass. Furthermore, the research shows that IMAN can be used to reliably predict subsequent periapsis times and locations over all aerobraking regimes. IMAN also yields accurate peak dynamic pressure and heating rates, critical for a successful corridor control strategy, comparable to navigation team reconstructed values. This research also provides the first instance of the utilization of the Unscented Kalman Filter for the purpose of estimating an actual spacecraft trajectory arc about another planet

Multiple-Object Space Surveillance Tracking Using Finite-Set Statistics

The dynamic tracking of objects is, in general, concerned with state estimation using imperfect data. Multiple object tracking adds the difficulty of encountering unknown associations between the collected data and the objects. State estimation of objects necessitates the prediction of uncertainty through nonlinear (in the general case) dynamical systems and the processing of nonlinear (in the general case) measurement data in order to provide corrections that refine the system uncertainty, where the uncertainty may be non-Gaussian in nature. The sensors, which provide the measurement data, are imperfect with possible misdetections, false alarms, and noise-affected data. The resulting measurements are inherently unassociated upon reception. In this paper, a Bayesian method for tracking an arbitrary, but known, number of objects is developed. The method is based on finite-set statistics coupled with finite mixture model representations of the multiobject probability density function. Instead of relying on first-moment approximations, such as the probability hypothesis density filter, to the full multiobject Bayesian posterior, as is often done for multiobject filtering, the proposed method operates directly on the exact Bayesian posterior. Results are presented for application of the method to the problem of tracking multiple space objects using synthetic line-of-sight data