Recent events in space, including the collision of Russias Cosmos 2251 satellite with Iridium 33 and Chinas Feng Yun 1C anti-satellite demonstration, have stressed the capabilities of Space Surveillance Network (SSN) and its ability to provide accurate and actionable impact probability estimates. The SSN network has the unique challenge of tracking more than 18,000 resident space objects (RSOs) and providing critical collision avoidance warnings to military, NASA, and commercial systems. However, due to the large number of RSOs and the limited number of sensors available to track them, it is impossible to maintain persistent surveillance. Observation gaps result in large propagation intervals between measurements and close approaches. Coupled with nonlinear RSO dynamics this results in difficulty in modeling the probability distribution functions (pdfs) of the RSO. In particular low-Earth orbiting (LEO) satellites are heavily influenced by atmospheric drag, which is very difficult to model accurately. A number of atmospheric models exist which can be classified as either empirical or physics-based models. The current Air Force standard is the High Accuracy Satellite Drag Model (HASDM), which is an empirical model based on observation of calibration satellites. These satellite observations are used to determine model parameters based on their orbit determination solutions. Atmospheric orbits are perturbed by a number of factors including drag coefficient, attitude, and shape of the space object. The satellites used for the HASDM model calibration process are chosen because of their relatively simple shapes, to minimize errors introduced due to shape miss-modeling. Under this requirement the number of calibration satellites that can be used for calibrating the atmospheric models is limited. Los Alamos National Laboratory (LANL) has established a research effort, called IMPACT (Integrated Modeling of Perturbations in Atmospheres for Conjunction Tracking), to improve impact assessment via improved physics-based modeling. As part of this effort calibration satellite observations are used to dynamically calibrate the physics-based model and to improve its forecasting capability. The observations are collected from a variety of sources, including from LANLs own Raven-class optical telescope. This system collects both astrometric and photometric data on space objects. The photometric data will be used to estimate the space objects attitude and shape. Non-resolved photometric data have been studied by many as a mechanism for space object characterization. Photometry is the measurement of an objects flux or apparent brightness measured over a wavelength band. The temporal variation of photometric measurements is referred to as photometric signature. The photometric optical signature of an object contains information about shape, attitude, size and material composition. This work focuses on the processing of the data collected with LANLs telescope in an effort to use photometric data to expand the number of space objects that can be used as calibration satellites. A nonlinear least squares is used to estimate the attitude and angular velocity of the space object; a number of real data examples are shown. Inactive space objects are used for the real data examples and good estimation results are shown.
Advanced Maui Optical and Space Surveillance Technologies Conference
- Pub Date:
- September 2014