Spacecraft Attitude Determination Simulation and Experiment to Improve the Efficiency of a Star Tracker

Knowing a spacecraft’s orientation is crucial for many vital functions. Attitude is often determined using a star tracker. Star tracker attitude determination must be fast and efficient given the limited on board computing resources. To determine its attitude, a star tracker must take an image of its environment, locate the stars in that image, recognize a pattern among those stars, match it with patterns in a catalog, and determine the rotation matrix that relates the spacecraft to the inertial frame. Searching through catalogs to match patterns is a costly step in this process. This work aims to develop a more efficient catalog and quantitatively select the best matching criteria to use. Programs to perform these steps were created to test the star tracker performance. Here, a new catalog generation method is presented. For this catalog, three parameters are necessary to find a certain match with an uncertainty of 1% for each parameter. This catalog requires over five times as many triangles as the existing catalog and three parameters instead of one, but only 39% as many stars. For this star tracker, the catalog requires more memory than an existing catalog, but is guaranteed to find a match on the first attempt, potentially making the new catalog faster. The size of the catalog decreases with larger fields of view, so catalogs for other star trackers may be smaller. Depending on the hardware and computing requirements of the mission, catalogs generated with the new method may be faster and more efficient

Earth Magnetosphere Model Investigations for Coupled Orbit-Attitude Space Debris Perturbations

As more objects are placed into orbit, collisions become increasingly more likely, leading to a so-called Kessler Syndrome: collisions between existing debris creates more debris, causing a cascading effect of larger amounts of debris being put into orbit, even in the absence of launches, making future space fairing difficult or impossible. Natural forces influence the orbit and attitude of uncontrolled debris objects. The natural plasma environment can lead to space object charging. The subsequent orbital movement in the geomagnetosphere induces Lorentz forces that act both on the orbit and attitude of the space object. Those forces have not been investigated thoroughly so far. Current physics-based models of the Earth\u27s magnetosphere examine the influence of the Sun\u27s corona and the Earth\u27s ionosphere on the plasma. This study looks at focusing the magnetosphere models in the near Earth region, specifically from low Earth orbit up to an altitude of 36000 km, to decrease computation time without significantly lowering the accuracy in the region of interest. The models that this study examines are a dipole, multipole, and multipole with plasma dynamics. The position of a charge deviates on the order of micrometers when comparing results from a dipole model and 7th degree multipole model

Multi-Target Tracking for SMARTnet: Multi-Layer Probability Hypothesis Filter for Near-Earth Object Tracking

In this paper, a modified version of the finite set statistics-based Probability Hypothesis Density (PHD) filter is developed specifically for the optical multi-target tracking of objects in the near-Earth realm for Space Situational Awareness (SAA). A two-step PHD filter is proposed in a modified version. One labeled PHD filter is used on the orthogonal image plane, in which linear dynamics in a fourparameter state is employed, forming so-called tracklets. Tracklets are associated sets of a few closelyspaced observations covering a negligible part of the overall orbit. Furthermore, tracklets are fed into a second PHD filter in a modified measurement update version, utilizing the full near-Earth astrodynamics with a six parameter state. In the modification, each tracklet leads to only one update in the PHD, but all observations within the tracklet are processed in the single target Markov transition process within the filter. In this case, the single target filter is an Extended Kalman Filter. In addition, the birth process that has been usually in typical SSA applications shifted to the birth step, forcing a data-driven birth with the disadvantage of a severe model mismatch, back to the propagation step, as in the original PHD filter formulation, avoiding the mismatch. In order to overcome the lack of probabilistic description availability (one of the triggers of the shift to the datadriven update step of previous authors), the data is preprocessed. This has the advantage that birth can employ traditional initial orbit determination methods and does not have to rely on the initialization with an incomplete state using, e.g., an admissible regions approach. The results are generated using the optical data of the DLR SMARTnet telescope network and are compared to the DLR BACARDI data processing

A review on hot-spot areas within the Cislunar region and upon the Moon surface, and methods to gather passive information from these regions

The Cislunar region is becoming a focal point of expansion over upcoming decades. Long-term Lunar infrastructure supporting Cislunar expansion must be located in key regions on the Moon\u27s surface and in space. The purpose of this research is to identify key regions of interest on and around the Moon by investigating the location of valuable resources and the destination of future missions. Once key regions are established, low-lunar orbit trajectories are analyzed to enable methods of passive information gain in identified key regions of interest. It has been found that the South Pole and Earth-sided craters are key regions on the Lunar surface in the near future. Furthermore, an analysis of low lunar orbit trajectories is completed and demonstrates a possible framework to service the South Pole region

Predicting Satellite Close Approaches Using Statistical Parameters in the Context of Artificial Intelligence

In order to ensure a sustainable use of low earth orbit in particular and near Earth space in general, reliable and effective close approach prediction be-tween space objects is key. Only this allows for efficient and timely colli-sion avoidance. Space Situational Awareness (SSA) for commercial and government missions will be facing the rapidly growing amount of small and potentially less agile satellites as well as debris in the near earth realm, such as the increase in CubeSat launches and upcoming large constellations. At the same time, space object detection capabilities are expected to increase significantly, allowing for the reliable detection of smaller objects, e.g. when the Air Force Space Fence radar becomes operational. In combination, the space object catalog is expected to increase tremendously in size. In this paper, we introduce an investigative approach based on the latest capabili-ties in artificial intelligence in fostering the potential for fast and accurate close approach predictions. We consider the study of statistical and infor-mation theory parameters in contrast and complementary to the classical probability of collision computation alone, in order to determine the feasi-bility of reliably predicting close approaches

The observing campaign on the deep-space debris WT1190F as a test case for short-warning NEO impacts

On 2015 November 13, the small artificial object designated WT1190F entered the Earth atmosphere above the Indian Ocean offshore Sri Lanka after being discovered as a possible new asteroid only a few weeks earlier. At ESA's SSA-NEO Coordination Centre we took advantage of this opportunity to organize a ground-based observational campaign, using WT1190F as a test case for a possible similar future event involving a natural asteroidal body. <P /

Realistic Sensor Tasking Strategies

Efficient sensor tasking is a crucial step in building up and maintaining a catalog of space objects at the highest possible orbit quality. Sensor resources are limited; sensor location and setup (hardware and processing software) influence the quality of observations for initial orbit determination or orbit improvement that can be obtained. Furthermore, improved sensing capabilities are expected to lead to an increase of objects that are sought to be maintained in a catalog, easily reaching over 100’000 objects. Sensor tasking methods hence need to be computationally efficient in order to be successfully applied to operational systems, and need to take realistic constraints, such as limited visibility of objects, time-varying probability of detection and the specific capabilities in software and hardware for the specific sensors into account. This paper shows a method to formulate sensor tasking as an optimization problem and introduces a new method to provide fast and computationally efficient real time, near optimal sensor tasking solutions. Simulations are preformed using the USSTRATCOM TLE catalog of all geosynchronous objects. The results are compared to state of the art observation strategies

Noise estimation and probability of detection in non-resolved images: application to space object observation

Charged Couple Device (CCD) technology is widely used in various scientific measurement contexts. CCD equipped cameras have revolutionized astronomy and space-related optical telescope measurements in recent years. They are also used in electroscopic measurements, e.g., in fields such as geology, biology, and medicine. The signal-to-noise ratio and the probability of detection are crucial to design experiments observation setups properly and to employ further mathematical methods for data exploitation such as, e.g. multi-target tracking methods. Previous attempts to correctly characterize the signal-to-noise ratio for star observations are revisited in this work and adapted for the application of near-Earth object observations and high precision measurements, leading to a modified CCD equation. Our formulation proposes a novel distribution of the signal noise that accurately accounts for the truncation noise and the presence of ambiguous pixels. These improvements are employed to derive the probability of detection and the SNR with significant improvements compared to existing formulations when ambiguous pixels are present

Quantifying uncertainties in signal position in non-resolved object images: application to space object observation

International audienceCharged Coupled Devices (CCDs) and subsequently Complementary metal-oxide-semiconductor (CMOS) detectors revolutionized scientific imaging. On both the CCD and CMOS detector, the generated images are degraded by inevitable noise. In many applications, such as in astronomy or for satellite tracking , only unresolved object images are available. Strategies to estimate the center of the non-resolved image their results are affected by the detector noise. The uncertainty in the center is classically estimated by running prohibitively costly Monte Carlo simulations, but in this paper, we propose analytic uncertainty estimates of the center position. The expressions that depend on the pixel size, the signal to noise ratio and the extension of the object signal relative to the pixel size are validated against rigorous Monte Carlo simulations with very satisfying results. Numerical tests show that our analytic expression is an efficient substitute to the Monte Carlo simulation thereby reducing computational cost